Selection of Memory for Data Storage in a Storage Network

ABSTRACT

Methods and apparatus for selection of memory devices in a distributed storage network. In an example, a computing device receives a data object for storage and selects a set of storage nodes of a plurality of sets of storage nodes for storing the data object. Selection of the set of storage nodes includes determining storage attributes associated with each set of storage nodes of the plurality of sets of storage nodes. Selection of the set of storage nodes additionally includes determining a storage preference associated with the data object, and comparing the storage preference with the storage attributes of the plurality of sets of storage nodes to determine a best match. Following selection of a set of storage nodes, the computing device facilitates storage of the data object in the selected set of storage nodes.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility patent application claims priority pursuant to 35 U.S.C. § 120 as a continuation of U.S. Utility patent application Ser. No. 17/151,249, entitled “SELECTION OF MEMORY IN A DISTRIBUTED DATA STORAGE NETWORK,” filed Jan. 18, 2021, issuing as U.S. Pat. No. 11,360,852 on Jun. 14, 2022, which is a continuation of U.S. Utility patent application Ser. No. 16/580,379, entitled “READ OPTIMIZED AND WRITE OPTIMIZED DS PROCESSING UNITS,” filed Sep. 24, 2019, abandoned, which is a continuation-in-part of U.S. Utility patent application Ser. No. 16/047,942, entitled “NAMESPACE AFFINITY AND FAILOVER FOR PROCESSING UNITS IN A DISPERSED STORAGE NETWORK,” filed Jul. 27, 2018, abandoned, which is a continuation-in-part of U.S. Utility patent application Ser. No. 15/224,839, entitled “NON-TEMPORARILY STORING TEMPORARILY STORED DATA IN A DISPERSED STORAGE NETWORK,” filed Aug. 1, 2016, issued as U.S. Pat. No. 10,102,068 on Oct. 16, 2018, which is a continuation of U.S. Utility application Ser. No. 14/792,898, entitled “NON-TEMPORARILY STORING TEMPORARILY STORED DATA IN A DISPERSED STORAGE NETWORK,” filed Jul. 7, 2015, issued as U.S. Pat. No. 9,407,292 on Aug. 2, 2016, which is a continuation of U.S. Utility application Ser. No. 13/889,557, entitled “NON-TEMPORARILY STORING TEMPORARILY STORED DATA IN A DISPERSED STORAGE NETWORK,” filed May 8, 2013, issued as U.S. Pat. No. 9,110,833 on Aug. 18, 2015, which claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 61/663,836, entitled “LOAD BALANCING ACCESS OF A DISTRIBUTED STORAGE AND TASK NETWORK,” filed Jun. 25, 2012, each of which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility patent application for all purposes.

BACKGROUND Technical Field of the Invention

This invention relates generally to computer networks and more particularly to selection of memory for distributed storage of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/or store data. Such computing devices range from wireless smart phones, laptops, tablets, personal computers (PC), workstations, and video game devices, to data centers that support millions of web searches, stock trades, or on-line purchases every day. In general, a computing device includes a central processing unit (CPU), a memory system, user input/output interfaces, peripheral device interfaces, and an interconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using “cloud computing” to perform one or more computing functions (e.g., a service, an application, an algorithm, an arithmetic logic function, etc.) on behalf of the computer. Further, for large services, applications, and/or functions, cloud computing may be performed by multiple cloud computing resources in a distributed manner to improve the response time for completion of the service, application, and/or function. For example, Hadoop is an open source software framework that supports distributed applications enabling application execution by thousands of computers.

In addition to cloud computing, a computer may use “cloud storage” as part of its memory system. As is known, cloud storage enables a user, via its computer, to store files, applications, etc., on an Internet storage system. The Internet storage system may include a RAID (redundant array of independent disks) system and/or a dispersed storage system that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system in accordance with the present disclosure;

FIG. 2 is a schematic block diagram of an embodiment of a computing core in accordance with the present disclosure;

FIG. 3 is a diagram of an example of a distributed storage and task processing in accordance with the present disclosure;

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing in accordance with the present disclosure;

FIG. 5 is a logic diagram of an example of a method for outbound DST processing in accordance with the present disclosure;

FIG. 6 is a schematic block diagram of an embodiment of a dispersed error encoding in accordance with the present disclosure;

FIG. 7 is a diagram of an example of a segment processing of the dispersed error encoding in accordance with the present disclosure;

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding in accordance with the present disclosure;

FIG. 9 is a diagram of an example of grouping selection processing of the outbound DST processing in accordance with the present disclosure;

FIG. 10 is a diagram of an example of converting data into slice groups in accordance with the present disclosure;

FIG. 11 is a schematic block diagram of an embodiment of a DST execution unit in accordance with the present disclosure;

FIG. 12 is a schematic block diagram of an example of operation of a DST execution unit in accordance with the present disclosure;

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing in accordance with the present disclosure;

FIG. 14 is a logic diagram of an example of a method for inbound DST processing in accordance with the present disclosure;

FIG. 15 is a diagram of an example of de-grouping selection processing of the inbound DST processing in accordance with the present disclosure;

FIG. 16 is a schematic block diagram of an embodiment of a dispersed error decoding in accordance with the present disclosure;

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of the dispersed error decoding in accordance with the present disclosure;

FIG. 18 is a diagram of an example of a de-segment processing of the dispersed error decoding in accordance with the present disclosure;

FIG. 19 is a diagram of an example of converting slice groups into data in accordance with the present disclosure;

FIG. 20 is a diagram of an example of a distributed storage within the distributed computing system in accordance with the present disclosure;

FIG. 21 is a schematic block diagram of an example of operation of outbound distributed storage and/or task (DST) processing for storing data in accordance with the present disclosure;

FIG. 22 is a schematic block diagram of an example of a dispersed error encoding for the example of FIG. 21 in accordance with the present disclosure;

FIG. 23 is a diagram of an example of converting data into pillar slice groups for storage in accordance with the present disclosure;

FIG. 24 is a schematic block diagram of an example of a storage operation of a DST execution unit in accordance with the present disclosure;

FIG. 25 is a schematic block diagram of an example of operation of inbound distributed storage and/or task (DST) processing for retrieving dispersed error encoded data in accordance with the present disclosure;

FIG. 26 is a schematic block diagram of an example of a dispersed error decoding for the example of FIG. 25 in accordance with the present disclosure;

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing a plurality of data and a plurality of task codes in accordance with the present disclosure;

FIG. 28 is a schematic block diagram of an example of the distributed computing system performing tasks on stored data in accordance with the present disclosure;

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module facilitating the example of FIG. 28 in accordance with the present disclosure;

FIG. 30 is a diagram of a specific example of the distributed computing system performing tasks on stored data in accordance with the present disclosure;

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30 in accordance with the present disclosure;

FIG. 32 is a diagram of an example of DST allocation information for the example of FIG. 30 in accordance with the present disclosure;

FIGS. 33-38 are schematic block diagrams of the DSTN module performing the example of FIG. 30 in accordance with the present disclosure;

FIG. 39 is a diagram of an example of combining result information into final results for the example of FIG. 30 in accordance with the present disclosure;

FIG. 40A is a schematic block diagram of another embodiment of a distributed computing system in accordance with the present disclosure;

FIG. 40B is a schematic block diagram of an embodiment of a dispersed storage network system in accordance with the present disclosure;

FIG. 40C is a flowchart illustrating an example of non-temporarily storing temporarily stored data in accordance with the present disclosure;

FIG. 41A is a schematic block diagram of another embodiment of a distributed computing system in accordance with the present disclosure;

FIG. 41B is a diagram illustrating an example of an affinity table in accordance with the present disclosure;

FIG. 41C is a flowchart illustrating an example of load-balancing in accordance with the present disclosure;

FIG. 42 is a flowchart illustrating another example of load-balancing in accordance with the present disclosure;

FIG. 43A is a schematic diagram illustrating another example of load-balancing in accordance with the present disclosure;

FIG. 43B is a schematic diagram illustrating another example of load-balancing in accordance with the present disclosure;

FIG. 43C is a flowchart illustrating another example of load-balancing in accordance with the present disclosure;

FIG. 44A is a schematic block diagram of another embodiment of a distributed storage and task client module in accordance with the present disclosure; and

FIG. 44B is a schematic block diagram of another embodiment of a dispersed storage network system in accordance with the present disclosure; and

FIG. 44C is a flowchart illustrating an example of storing data in accordance with the present disclosure.

DETAILED DESCRIPTION

FIG. 1 is a schematic block diagram of an embodiment of a distributed computing system 10 that includes a user device 12 and/or a user device 14, a distributed storage and/or task (DST) processing unit 16 (DS processing unit), a distributed storage and/or task network (DSTN) managing unit 18 (DSN managing unit), a DST integrity processing unit 20 (DS integrity processing unit), and a distributed storage and/or task network (DSTN) module 22 (DSN module). The components of the distributed computing system 10 are coupled via a network 24, which may include one or more wireless and/or wire lined communication systems; one or more private intranet systems and/or public internet systems; and/or one or more local area networks (LAN) and/or wide area networks (WAN).

The DSTN module 22 includes a plurality of distributed storage and/or task (DST) execution units 36 (storage units) that may be located at geographically different sites (e.g., one in Chicago, one in Milwaukee, etc.). Each of the DST execution units is operable to store dispersed error encoded data and/or to execute, in a distributed manner, one or more tasks on data. The tasks may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc.

Each of the user devices 12-14, the DST processing unit 16, the DSTN managing unit 18, and the DST integrity processing unit 20 include a computing core 26 and may be a portable computing device and/or a fixed computing device. A portable computing device may be a social networking device, a gaming device, a cell phone, a smart phone, a personal digital assistant, a digital music player, a digital video player, a laptop computer, a handheld computer, a tablet, a video game controller, and/or any other portable device that includes a computing core. A fixed computing device may be a personal computer (PC), a computer server, a cable set-top box, a satellite receiver, a television set, a printer, a fax machine, home entertainment equipment, a video game console, and/or any type of home or office computing equipment. User device 12 and DST processing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includes software and/or hardware to support one or more communication links via the network 24 indirectly and/or directly. For example, interface 30 supports a communication link (e.g., wired, wireless, direct, via a LAN, via the network 24, etc.) between user device 14 and the DST processing unit 16. As another example, interface 32 supports communication links (e.g., a wired connection, a wireless connection, a LAN connection, and/or any other type of connection to/from the network 24) between user device 12 and the DSTN module 22 and between the DST processing unit 16 and the DSTN module 22. As yet another example, interface 33 supports a communication link for each of the DSTN managing unit 18 and DST integrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersed storage (DS) error encoded data storage and retrieval, to support distributed task processing on received data, and/or to support distributed task processing on stored data. In general, and with respect to DS error encoded data storage and retrieval, the distributed computing system 10 supports three primary operations: storage management, data storage and retrieval (an example of which will be discussed with reference to FIGS. 20-26), and data storage integrity verification. In accordance with these three primary functions, data can be encoded, distributedly stored in physically different locations, and subsequently retrieved in a reliable and secure manner. Such a system is tolerant of a significant number of failures (e.g., up to a failure level, which may be greater than or equal to a pillar width minus a decode threshold minus one) that may result from individual storage device failures and/or network equipment failures without loss of data and without the need for a redundant or backup copy. Further, the system allows the data to be stored for an indefinite period of time without data loss and does so in a secure manner (e.g., the system is very resistant to attempts at hacking the data).

The second primary function (i.e., distributed data storage and retrieval) begins and ends with a user device 12-14. For instance, if a second type of user device 14 has data 40 to store in the DSTN module 22, it sends the data 40 to the DST processing unit 16 via its interface 30. The interface 30 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). In addition, the interface 30 may attach a user identification code (ID) to the data 40.

To support storage management, the DSTN managing unit 18 performs DS management services. One such DS management service includes the DSTN managing unit 18 establishing distributed data storage parameters (e.g., vault creation, distributed storage parameters, security parameters, billing information, user profile information, etc.) for a user device 12-14 individually or as part of a group of user devices. For example, the DSTN managing unit 18 coordinates creation of a vault (e.g., a virtual memory block) within memory of the DSTN module 22 for a user device, a group of devices, or for public access and establishes per vault dispersed storage (DS) error encoding parameters for a vault. The DSTN managing unit 18 may facilitate storage of DS error encoding parameters for each vault of a plurality of vaults by updating registry information for the distributed computing system 10. The facilitating includes storing updated registry information in one or more of the DSTN module 22, the user device 12, the DST processing unit 16, and the DST integrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage error coding parameters) include data segmenting information (e.g., how many segments data (e.g., a file, a group of files, a data block, etc.) is divided into), segment security information (e.g., per segment encryption, compression, integrity checksum, etc.), error coding information (e.g., pillar width, decode threshold, read threshold, write threshold, etc.), slicing information (e.g., the number of encoded data slices that will be created for each data segment); and slice security information (e.g., per encoded data slice encryption, compression, integrity checksum, etc.).

The DSTN managing unit 18 creates and stores user profile information (e.g., an access control list (ACL)) in local memory and/or within memory of the DSTN module 22. The user profile information includes authentication information, permissions, and/or the security parameters. The security parameters may include encryption/decryption scheme, one or more encryption keys, key generation scheme, and/or data encoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particular user, a user group, a vault access, public vault access, etc. For instance, the DSTN managing unit 18 tracks the number of times a user accesses a private vault and/or public vaults, which can be used to generate a per-access billing information. In another instance, the DSTN managing unit 18 tracks the amount of data stored and/or retrieved by a user device and/or a user group, which can be used to generate a per-data-amount billing information.

Another DS management service includes the DSTN managing unit 18 performing network operations, network administration, and/or network maintenance. Network operations include authenticating user data allocation requests (e.g., read and/or write requests), managing creation of vaults, establishing authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system 10, and/or establishing authentication credentials for DST execution units 36. Network administration includes monitoring devices and/or units for failures, maintaining vault information, determining device and/or unit activation status, determining device and/or unit loading, and/or determining any other system level operation that affects the performance level of the system 10. Network maintenance includes facilitating replacing, upgrading, repairing, and/or expanding a device and/or unit of the system 10.

To support data storage integrity verification within the distributed computing system 10, the DST integrity processing unit 20 performs rebuilding of ‘bad’ or missing encoded data slices. At a high level, the DST integrity processing unit 20 performs rebuilding by periodically attempting to retrieve/list encoded data slices, and/or slice names of the encoded data slices, from the DSTN module 22. For retrieved encoded slices, they are checked for errors due to data corruption, outdated version, etc. If a slice includes an error, it is flagged as a ‘bad’ slice. For encoded data slices that were not received and/or not listed, they are flagged as missing slices. Bad and/or missing slices are subsequently rebuilt using other retrieved encoded data slices that are deemed to be good slices to produce rebuilt slices. The rebuilt slices are stored in memory of the DSTN module 22. Note that the DST integrity processing unit 20 may be a separate unit as shown, it may be included in the DSTN module 22, it may be included in the DST processing unit 16, and/or distributed among the DST execution units 36.

To support distributed task processing on received data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task processing) management and DST execution on received data (an example of which will be discussed with reference to FIGS. 3-19). With respect to the storage portion of the DST management, the DSTN managing unit 18 functions as previously described. With respect to the tasking processing of the DST management, the DSTN managing unit 18 performs distributed task processing (DTP) management services. One such DTP management service includes the DSTN managing unit 18 establishing DTP parameters (e.g., user-vault affiliation information, billing information, user-task information, etc.) for a user device 12-14 individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18 performing DTP network operations, network administration (which is essentially the same as described above), and/or network maintenance (which is essentially the same as described above). Network operations include, but are not limited to, authenticating user task processing requests (e.g., valid request, valid user, etc.), authenticating results and/or partial results, establishing DTP authentication credentials for user devices, adding/deleting components (e.g., user devices, DST execution units, and/or DST processing units) from the distributed computing system, and/or establishing DTP authentication credentials for DST execution units.

To support distributed task processing on stored data, the distributed computing system 10 has two primary operations: DST (distributed storage and/or task) management and DST execution on stored data. With respect to the DST execution on stored data, if the second type of user device 14 has a task request 38 for execution by the DSTN module 22, it sends the task request 38 to the DST processing unit 16 via its interface 30. An example of DST execution on stored data will be discussed in greater detail with reference to FIGS. 27-39. With respect to the DST management, it is substantially similar to the DST management to support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core 26 that includes a processing module 50, a memory controller 52, main memory 54, a video graphics processing unit 55, an input/output (IO) controller 56, a peripheral component interconnect (PCI) interface 58, an IO interface module 60, at least one IO device interface module 62, a read only memory (ROM) basic input output system (BIOS) 64, and one or more memory interface modules. The one or more memory interface module(s) includes one or more of a universal serial bus (USB) interface module 66, a host bus adapter (HBA) interface module 68, a network interface module 70, a flash interface module 72, a hard drive interface module 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operating system (OS) file system interface (e.g., network file system (NFS), flash file system (FFS), disk file system (DFS), file transfer protocol (FTP), web-based distributed authoring and versioning (WebDAV), etc.) and/or a block memory interface (e.g., small computer system interface (SCSI), internet small computer system interface (iSCSI), etc.). The DSTN interface module 76 and/or the network interface module 70 may function as the interface 30 of the user device 14 of FIG. 1. Further note that the IO device interface module 62 and/or the memory interface modules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing system performing a distributed storage and task processing operation. The distributed computing system includes a DST (distributed storage and/or task) client module 34 (which may be in user device 14 and/or in DST processing unit 16 of FIG. 1), a network 24, a plurality of DST execution units 1-n that includes two or more DST execution units 36 of FIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), a DST managing module (not shown), and a DST integrity verification module (not shown). The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. Each of the DST execution units 1-n includes a controller 86, a processing module 84, memory 88, a DT (distributed task) execution module 90, and a DST client module 34.

In an example of operation, the DST client module 34 receives data 92 and one or more tasks 94 to be performed upon the data 92. The data 92 may be of any size and of any content, where, due to the size (e.g., greater than a few Terabytes), the content (e.g., secure data, etc.), and/or task(s) (e.g., MIPS intensive), distributed processing of the task(s) on the data is desired. For example, the data 92 may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80 receives the data 92 and the task(s) 94. The outbound DST processing section 80 processes the data 92 to produce slice groupings 96. As an example of such processing, the outbound DST processing section 80 partitions the data 92 into a plurality of data partitions. For each data partition, the outbound DST processing section 80 dispersed storage (DS) error encodes the data partition to produce encoded data slices and groups the encoded data slices into a slice grouping 96. In addition, the outbound DST processing section 80 partitions the task 94 into partial tasks 98, where the number of partial tasks 98 may correspond to the number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24, the slice groupings 96 and the partial tasks 98 to the DST execution units 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DST processing section 80 sends slice group 1 and partial task 1 to DST execution unit 1. As another example, the outbound DST processing section 80 sends slice group #n and partial task #n to DST execution unit #n.

Each DST execution unit performs its partial task 98 upon its slice group 96 to produce partial results 102. For example, DST execution unit #1 performs partial task #1 on slice group #1 to produce a partial result #1, for results. As a more specific example, slice group #1 corresponds to a data partition of a series of digital books and the partial task #1 corresponds to searching for specific phrases, recording where the phrase is found, and establishing a phrase count. In this more specific example, the partial result #1 includes information as to where the phrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, the DST execution units send, via the network 24, their partial results 102 to the inbound DST processing section 82 of the DST client module 34. The inbound DST processing section 82 processes the received partial results 102 to produce a result 104. Continuing with the specific example of the preceding paragraph, the inbound DST processing section 82 combines the phrase count from each of the DST execution units 36 to produce a total phrase count. In addition, the inbound DST processing section 82 combines the ‘where the phrase was found’ information from each of the DST execution units 36 within their respective data partitions to produce ‘where the phrase was found’ information for the series of digital books.

In another example of operation, the DST client module 34 requests retrieval of stored data within the memory of the DST execution units 36 (e.g., memory of the DSTN module). In this example, the task 94 is retrieve data stored in the memory of the DSTN module. Accordingly, the outbound DST processing section 80 converts the task 94 into a plurality of partial tasks 98 and sends the partial tasks 98 to the respective DST execution units 1-n.

In response to the partial task 98 of retrieving stored data, a DST execution unit 36 identifies the corresponding encoded data slices 100 and retrieves them. For example, DST execution unit #1 receives partial task #1 and retrieves, in response thereto, retrieved slices #1. The DST execution units 36 send their respective retrieved slices 100 to the inbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100 into data 92. For example, the inbound DST processing section 82 de-groups the retrieved slices 100 to produce encoded slices per data partition. The inbound DST processing section 82 then DS error decodes the encoded slices per data partition to produce data partitions. The inbound DST processing section 82 de-partitions the data partitions to recapture the data 92.

FIG. 4 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., a plurality of n DST execution units 36) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 partitions data 92 into a plurality of data partitions 120. The number of partitions and the size of the partitions may be selected by the control module 116 via control 160 based on the data 92 (e.g., its size, its content, etc.), a corresponding task 94 to be performed (e.g., simple, complex, single step, multiple steps, etc.), DS encoding parameters (e.g., pillar width, decode threshold, write threshold, segment security parameters, slice security parameters, etc.), capabilities of the DST execution units 36 (e.g., processing resources, availability of processing recourses, etc.), and/or as may be inputted by a user, system administrator, or other operator (human or automated). For example, the data partitioning module 110 partitions the data 92 (e.g., 100 Terabytes) into 100,000 data segments, each being 1 Gigabyte in size. Alternatively, the data partitioning module 110 partitions the data 92 into a plurality of data segments, where some of data segments are of a different size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in a serial manner, a parallel manner, and/or a combination thereof. For each data partition 120, the DS error encoding module 112 DS error encodes the data partition 120 in accordance with control information 160 from the control module 116 to produce encoded data slices 122. The DS error encoding includes segmenting the data partition into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC), etc.). The control information 160 indicates which steps of the DS error encoding are active for a given data partition and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The grouping selector module 114 groups the encoded slices 122 of a data partition into a set of slice groupings 96. The number of slice groupings corresponds to the number of DST execution units 36 identified for a particular task 94. For example, if five DST execution units 36 are identified for the particular task 94, the grouping selector module groups the encoded slices 122 of a data partition into five slice groupings 96. The grouping selector module 114 outputs the slice groupings 96 to the corresponding DST execution units 36 via the network 24.

The distributed task control module 118 receives the task 94 and converts the task 94 into a set of partial tasks 98. For example, the distributed task control module 118 receives a task to find where in the data (e.g., a series of books) a phrase occurs and a total count of the phrase usage in the data. In this example, the distributed task control module 118 replicates the task 94 for each DST execution unit 36 to produce the partial tasks 98. In another example, the distributed task control module 118 receives a task to find where in the data a first phrase occurs, where in the data a second phrase occurs, and a total count for each phrase usage in the data. In this example, the distributed task control module 118 generates a first set of partial tasks 98 for finding and counting the first phrase and a second set of partial tasks for finding and counting the second phrase. The distributed task control module 118 sends respective first and/or second partial tasks 98 to each DST execution unit 36.

FIG. 5 is a logic diagram of an example of a method for outbound distributed storage and task (DST) processing that begins at step 126 where a DST client module receives data and one or more corresponding tasks. The method continues at step 128 where the DST client module determines a number of DST units to support the task for one or more data partitions. For example, the DST client module may determine the number of DST units to support the task based on the size of the data, the requested task, the content of the data, a predetermined number (e.g., user indicated, system administrator determined, etc.), available DST units, capability of the DST units, and/or any other factor regarding distributed task processing of the data. The DST client module may select the same DST units for each data partition, may select different DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determines processing parameters of the data based on the number of DST units selected for distributed task processing. The processing parameters include data partitioning information, DS encoding parameters, and/or slice grouping information. The data partitioning information includes a number of data partitions, size of each data partition, and/or organization of the data partitions (e.g., number of data blocks in a partition, the size of the data blocks, and arrangement of the data blocks). The DS encoding parameters include segmenting information, segment security information, error encoding information (e.g., dispersed storage error encoding function parameters including one or more of pillar width, decode threshold, write threshold, read threshold, generator matrix), slicing information, and/or per slice security information. The slice grouping information includes information regarding how to arrange the encoded data slices into groups for the selected DST units. As a specific example, if the DST client module determines that five DST units are needed to support the task, then it determines that the error encoding parameters include a pillar width of five and a decode threshold of three.

The method continues at step 132 where the DST client module determines task partitioning information (e.g., how to partition the tasks) based on the selected DST units and data processing parameters. The data processing parameters include the processing parameters and DST unit capability information. The DST unit capability information includes the number of DT (distributed task) execution units, execution capabilities of each DT execution unit (e.g., MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.)), and/or any information germane to executing one or more tasks.

The method continues at step 134 where the DST client module processes the data in accordance with the processing parameters to produce slice groupings. The method continues at step 136 where the DST client module partitions the task based on the task partitioning information to produce a set of partial tasks. The method continues at step 138 where the DST client module sends the slice groupings and the corresponding partial tasks to respective DST units.

FIG. 6 is a schematic block diagram of an embodiment of the dispersed storage (DS) error encoding module 112 of an outbound distributed storage and task (DST) processing section. The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives a data partition 120 from a data partitioning module and receives segmenting information as the control information 160 from the control module 116. The segmenting information indicates how the segment processing module 142 is to segment the data partition 120. For example, the segmenting information indicates how many rows to segment the data based on a decode threshold of an error encoding scheme, indicates how many columns to segment the data into based on a number and size of data blocks within the data partition 120, and indicates how many columns to include in a data segment 152. The segment processing module 142 segments the data 120 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., cyclic redundancy check (CRC), etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it may compress a data segment 152, encrypt the compressed data segment, and generate a CRC value for the encrypted data segment to produce a secure data segment 154. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments 154 in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters (e.g., also referred to as dispersed storage error coding parameters) include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an online coding algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment 154 to produce an encoded data segment 156.

The slicing module 148 slices the encoded data segment 156 in accordance with the pillar width of the error correction encoding parameters received as control information 160. For example, if the pillar width is five, the slicing module 148 slices an encoded data segment 156 into a set of five encoded data slices. As such, for a plurality of encoded data segments 156 for a given data partition, the slicing module outputs a plurality of sets of encoded data slices 158.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice 158 based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it compresses an encoded data slice 158, encrypts the compressed encoded data slice, and generates a CRC value for the encrypted encoded data slice to produce a secure encoded data slice 122. When the per slice security processing module 150 is not enabled, it passes the encoded data slices 158 or is bypassed such that the encoded data slices 158 are the output of the DS error encoding module 112. Note that the control module 116 may be omitted and each module stores its own parameters.

FIG. 7 is a diagram of an example of a segment processing of a dispersed storage (DS) error encoding module. In this example, a segment processing module 142 receives a data partition 120 that includes 45 data blocks (e.g., d1-d45), receives segmenting information (i.e., control information 160) from a control module, and segments the data partition 120 in accordance with the control information 160 to produce data segments 152. Each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data. As previously mentioned, the segmenting information indicates how many rows to segment the data partition into, indicates how many columns to segment the data partition into, and indicates how many columns to include in a data segment.

In this example, the decode threshold of the error encoding scheme is three; as such the number of rows to divide the data partition into is three. The number of columns for each row is set to 15, which is based on the number and size of data blocks. The data blocks of the data partition are arranged in rows and columns in a sequential order (i.e., the first row includes the first 15 data blocks; the second row includes the second 15 data blocks; and the third row includes the last 15 data blocks).

With the data blocks arranged into the desired sequential order, they are divided into data segments based on the segmenting information. In this example, the data partition is divided into 8 data segments; the first 7 include 2 columns of three rows and the last includes 1 column of three rows. Note that the first row of the 8 data segments is in sequential order of the first 15 data blocks; the second row of the 8 data segments in sequential order of the second 15 data blocks; and the third row of the 8 data segments in sequential order of the last 15 data blocks. Note that the number of data blocks, the grouping of the data blocks into segments, and size of the data blocks may vary to accommodate the desired distributed task processing function.

FIG. 8 is a diagram of an example of error encoding and slicing processing of the dispersed error encoding processing the data segments of FIG. 7. In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words for encoding: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

In operation, an error encoding module 146 and a slicing module 148 convert each data segment into a set of encoded data slices in accordance with error correction encoding parameters as control information 160. More specifically, when the error correction encoding parameters indicate a unity matrix Reed-Solomon based encoding algorithm, 5 pillars, and decode threshold of 3, the first three encoded data slices of the set of encoded data slices for a data segment are substantially similar to the corresponding word of the data segment. For instance, when the unity matrix Reed-Solomon based encoding algorithm is applied to data segment 1, the content of the first encoded data slice (DS1_d1&2) of the first set of encoded data slices (e.g., corresponding to data segment 1) is substantially similar to content of the first word (e.g., d1 & d2); the content of the second encoded data slice (DS1_d16&17) of the first set of encoded data slices is substantially similar to content of the second word (e.g., d16 & d17); and the content of the third encoded data slice (DS1_d31&32) of the first set of encoded data slices is substantially similar to content of the third word (e.g., d31 & d32).

The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the first set of encoded data slices include error correction data based on the first-third words of the first data segment. With such an encoding and slicing scheme, retrieving any three of the five encoded data slices allows the data segment to be accurately reconstructed.

The encoding and slicing of data segments 2-7 yield sets of encoded data slices similar to the set of encoded data slices of data segment 1. For instance, the content of the first encoded data slice (DS2_d3&4) of the second set of encoded data slices (e.g., corresponding to data segment 2) is substantially similar to content of the first word (e.g., d3 & d4); the content of the second encoded data slice (DS2_d18&19) of the second set of encoded data slices is substantially similar to content of the second word (e.g., d18 & d19); and the content of the third encoded data slice (DS2_d33&34) of the second set of encoded data slices is substantially similar to content of the third word (e.g., d33 & d34). The content of the fourth and fifth encoded data slices (e.g., ES1_1 and ES1_2) of the second set of encoded data slices includes error correction data based on the first-third words of the second data segment.

FIG. 9 is a diagram of an example of grouping selection processing of an outbound distributed storage and task (DST) processing in accordance with group selection information as control information 160 from a control module. Encoded slices for data partition 122 are grouped in accordance with the control information 160 to produce slice groupings 96. In this example, a grouping selector module 114 organizes the encoded data slices into five slice groupings (e.g., one for each DST execution unit of a distributed storage and task network (DSTN) module). As a specific example, the grouping selector module 114 creates a first slice grouping for a DST execution unit #1, which includes first encoded slices of each of the sets of encoded slices. As such, the first DST execution unit receives encoded data slices corresponding to data blocks 1-15 (e.g., encoded data slices of contiguous data).

The grouping selector module 114 also creates a second slice grouping for a DST execution unit #2, which includes second encoded slices of each of the sets of encoded slices. As such, the second DST execution unit receives encoded data slices corresponding to data blocks 16-30. The grouping selector module 114 further creates a third slice grouping for DST execution unit #3, which includes third encoded slices of each of the sets of encoded slices. As such, the third DST execution unit receives encoded data slices corresponding to data blocks 31-45.

The grouping selector module 114 creates a fourth slice grouping for DST execution unit #4, which includes fourth encoded slices of each of the sets of encoded slices. As such, the fourth DST execution unit receives encoded data slices corresponding to first error encoding information (e.g., encoded data slices of error coding (EC) data). The grouping selector module 114 further creates a fifth slice grouping for DST execution unit #5, which includes fifth encoded slices of each of the sets of encoded slices. As such, the fifth DST execution unit receives encoded data slices corresponding to second error encoding information.

FIG. 10 is a diagram of an example of converting data 92 into slice groups that expands on the preceding figures. As shown, the data 92 is partitioned in accordance with a partitioning function 164 into a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunkset of data) is encoded and grouped into slice groupings as previously discussed by an encoding and grouping function 166. For a given data partition, the slice groupings are sent to distributed storage and task (DST) execution units. From data partition to data partition, the ordering of the slice groupings to the DST execution units may vary.

For example, the slice groupings of data partition #1 is sent to the DST execution units such that the first DST execution receives first encoded data slices of each of the sets of encoded data slices, which corresponds to a first continuous data chunk of the first data partition (e.g., refer to FIG. 9), a second DST execution receives second encoded data slices of each of the sets of encoded data slices, which corresponds to a second continuous data chunk of the first data partition, etc.

For the second data partition, the slice groupings may be sent to the DST execution units in a different order than it was done for the first data partition. For instance, the first slice grouping of the second data partition (e.g., slice group 2_1) is sent to the second DST execution unit; the second slice grouping of the second data partition (e.g., slice group 2_2) is sent to the third DST execution unit; the third slice grouping of the second data partition (e.g., slice group 23) is sent to the fourth DST execution unit; the fourth slice grouping of the second data partition (e.g., slice group 2_4, which includes first error coding information) is sent to the fifth DST execution unit; and the fifth slice grouping of the second data partition (e.g., slice group 2_5, which includes second error coding information) is sent to the first DST execution unit.

The pattern of sending the slice groupings to the set of DST execution units may vary in a predicted pattern, a random pattern, and/or a combination thereof from data partition to data partition. In addition, from data partition to data partition, the set of DST execution units may change. For example, for the first data partition, DST execution units 1-5 may be used; for the second data partition, DST execution units 6-10 may be used; for the third data partition, DST execution units 3-7 may be used; etc. As is also shown, the task 94 is divided into partial tasks that are sent to the DST execution units in conjunction with the slice groupings of the data partitions.

FIG. 11 is a schematic block diagram of an embodiment of a DST (distributed storage and/or task) execution unit that includes an interface 169, a controller 86, memory 88, one or more DT (distributed task) execution modules 90, and a DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a slice group, the DST execution module receives a slice grouping 96 (e.g., slice group #1) via interface 169. The slice grouping 96 includes, per partition, encoded data slices of contiguous data or encoded data slices of error coding (EC) data. For slice group #1, the DST execution module receives encoded data slices of contiguous data for partitions #1 and #x (and potentially others between 3 and x) and receives encoded data slices of EC data for partitions #2 and #3 (and potentially others between 3 and x). Examples of encoded data slices of contiguous data and encoded data slices of error coding (EC) data are discussed with reference to FIG. 9. The memory 88 stores the encoded data slices of slice groupings 96 in accordance with memory control information 174 it receives from the controller 86.

The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on a partial task(s) 98 and distributed computing information (e.g., user information (e.g., user ID, distributed computing permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, temporary storage for task processing, etc.), task validation information, etc.). For example, the controller 86 interprets the partial task(s) 98 in light of the distributed computing information to determine whether a requestor is authorized to perform the task 98, is authorized to access the data, and/or is authorized to perform the task on this particular data. When the requestor is authorized, the controller 86 determines, based on the task 98 and/or another input, whether the encoded data slices of the slice grouping 96 are to be temporarily stored or permanently stored. Based on the foregoing, the controller 86 generates the memory control information 174 to write the encoded data slices of the slice grouping 96 into the memory 88 and to indicate whether the slice grouping 96 is permanently stored or temporarily stored.

With the slice grouping 96 stored in the memory 88, the controller 86 facilitates execution of the partial task(s) 98. In an example, the controller 86 interprets the partial task 98 in light of the capabilities of the DT execution module(s) 90. The capabilities include one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, etc. If the controller 86 determines that the DT execution module(s) 90 have sufficient capabilities, it generates task control information 176.

The task control information 176 may be a generic instruction (e.g., perform the task on the stored slice grouping) or a series of operational codes. In the former instance, the DT execution module 90 includes a co-processor function specifically configured (fixed or programmed) to perform the desired task 98. In the latter instance, the DT execution module 90 includes a general processor topology where the controller stores an algorithm corresponding to the particular task 98. In this instance, the controller 86 provides the operational codes (e.g., assembly language, source code of a programming language, object code, etc.) of the algorithm to the DT execution module 90 for execution.

Depending on the nature of the task 98, the DT execution module 90 may generate intermediate partial results 102 that are stored in the memory 88 or in a cache memory (not shown) within the DT execution module 90. In either case, when the DT execution module 90 completes execution of the partial task 98, it outputs one or more partial results 102. The partial results 102 may also be stored in memory 88.

If, when the controller 86 is interpreting whether capabilities of the DT execution module(s) 90 can support the partial task 98, the controller 86 determines that the DT execution module(s) 90 cannot adequately support the task 98 (e.g., does not have the right resources, does not have sufficient available resources, available resources would be too slow, etc.), it then determines whether the partial task 98 should be fully offloaded or partially offloaded.

If the controller 86 determines that the partial task 98 should be fully offloaded, it generates DST control information 178 and provides it to the DST client module 34. The DST control information 178 includes the partial task 98, memory storage information regarding the slice grouping 96, and distribution instructions. The distribution instructions instruct the DST client module 34 to divide the partial task 98 into sub-partial tasks 172, to divide the slice grouping 96 into sub-slice groupings 170, and identify other DST execution units. The DST client module 34 functions in a similar manner as the DST client module 34 of FIGS. 3-10 to produce the sub-partial tasks 172 and the sub-slice groupings 170 in accordance with the distribution instructions.

The DST client module 34 receives DST feedback 168 (e.g., sub-partial results), via the interface 169, from the DST execution units to which the task was offloaded. The DST client module 34 provides the sub-partial results to the DST execution unit, which processes the sub-partial results to produce the partial result(s) 102.

If the controller 86 determines that the partial task 98 should be partially offloaded, it determines what portion of the task 98 and/or slice grouping 96 should be processed locally and what should be offloaded. For the portion that is being locally processed, the controller 86 generates task control information 176 as previously discussed. For the portion that is being offloaded, the controller 86 generates DST control information 178 as previously discussed.

When the DST client module 34 receives DST feedback 168 (e.g., sub-partial results) from the DST executions units to which a portion of the task was offloaded, it provides the sub-partial results to the DT execution module 90. The DT execution module 90 processes the sub-partial results with the sub-partial results it created to produce the partial result(s) 102.

The memory 88 may be further utilized to retrieve one or more of stored slices 100, stored results 104, partial results 102 when the DT execution module 90 stores partial results 102 and/or results 104 in the memory 88. For example, when the partial task 98 includes a retrieval request, the controller 86 outputs the memory control 174 to the memory 88 to facilitate retrieval of slices 100 and/or results 104.

FIG. 12 is a schematic block diagram of an example of operation of a distributed storage and task (DST) execution unit storing encoded data slices and executing a task thereon. To store the encoded data slices of a partition 1 of slice grouping 1, a controller 86 generates write commands as memory control information 174 such that the encoded slices are stored in desired locations (e.g., permanent or temporary) within memory 88.

Once the encoded slices are stored, the controller 86 provides task control information 176 to a distributed task (DT) execution module 90. As a first step of executing the task in accordance with the task control information 176, the DT execution module 90 retrieves the encoded slices from memory 88. The DT execution module 90 then reconstructs contiguous data blocks of a data partition. As shown for this example, reconstructed contiguous data blocks of data partition 1 include data blocks 1-15 (e.g., d1-d15).

With the contiguous data blocks reconstructed, the DT execution module 90 performs the task on the reconstructed contiguous data blocks. For example, the task may be to search the reconstructed contiguous data blocks for a particular word or phrase, identify where in the reconstructed contiguous data blocks the particular word or phrase occurred, and/or count the occurrences of the particular word or phrase on the reconstructed contiguous data blocks. The DST execution unit continues in a similar manner for the encoded data slices of other partitions in slice grouping 1. Note that with using the unity matrix error encoding scheme previously discussed, if the encoded data slices of contiguous data are uncorrupted, the decoding of them is a relatively straightforward process of extracting the data.

If, however, an encoded data slice of contiguous data is corrupted (or missing), it can be rebuilt by accessing other DST execution units that are storing the other encoded data slices of the set of encoded data slices of the corrupted encoded data slice. In this instance, the DST execution unit having the corrupted encoded data slices retrieves at least three encoded data slices (of contiguous data and of error coding data) in the set from the other DST execution units (recall for this example, the pillar width is 5 and the decode threshold is 3). The DST execution unit decodes the retrieved data slices using the DS error encoding parameters to recapture the corresponding data segment. The DST execution unit then re-encodes the data segment using the DS error encoding parameters to rebuild the corrupted encoded data slice. Once the encoded data slice is rebuilt, the DST execution unit functions as previously described.

FIG. 13 is a schematic block diagram of an embodiment of an inbound distributed storage and/or task (DST) processing section 82 of a DST client module coupled to DST execution units of a distributed storage and task network (DSTN) module via a network 24. The inbound DST processing section 82 includes a de-grouping module 180, a DS (dispersed storage) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of outbound DST processing section or may be the same modules.

In an example of operation, the DST execution units have completed execution of corresponding partial tasks on the corresponding slice groupings to produce partial results 102. The inbound DST processing section 82 receives the partial results 102 via the distributed task control module 188. The inbound DST processing section 82 then processes the partial results 102 to produce a final result, or results 104. For example, if the task was to find a specific word or phrase within data, the partial results 102 indicate where in each of the prescribed portions of the data the corresponding DST execution units found the specific word or phrase. The distributed task control module 188 combines the individual partial results 102 for the corresponding portions of the data into a final result 104 for the data as a whole.

In another example of operation, the inbound DST processing section 82 is retrieving stored data from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices 100 corresponding to the data retrieval requests. The de-grouping module 180 receives retrieved slices 100 and de-groups them to produce encoded data slices per data partition 122. The DS error decoding module 182 decodes, in accordance with DS error encoding parameters, the encoded data slices per data partition 122 to produce data partitions 120.

The data de-partitioning module 184 combines the data partitions 120 into the data 92. The control module 186 controls the conversion of retrieved slices 100 into the data 92 using control signals 190 to each of the modules. For instance, the control module 186 provides de-grouping information to the de-grouping module 180, provides the DS error encoding parameters to the DS error decoding module 182, and provides de-partitioning information to the data de-partitioning module 184.

FIG. 14 is a logic diagram of an example of a method that is executable by distributed storage and task (DST) client module regarding inbound DST processing. The method begins at step 194 where the DST client module receives partial results. The method continues at step 196 where the DST client module retrieves the task corresponding to the partial results. For example, the partial results include header information that identifies the requesting entity, which correlates to the requested task.

The method continues at step 198 where the DST client module determines result processing information based on the task. For example, if the task were to identify a particular word or phrase within the data, the result processing information would indicate to aggregate the partial results for the corresponding portions of the data to produce the final result. As another example, if the task were to count the occurrences of a particular word or phrase within the data, results of processing the information would indicate to add the partial results to produce the final results. The method continues at step 200 where the DST client module processes the partial results in accordance with the result processing information to produce the final result or results.

FIG. 15 is a diagram of an example of de-grouping selection processing of an inbound distributed storage and task (DST) processing section of a DST client module. In general, this is an inverse process of the grouping module of the outbound DST processing section of FIG. 9. Accordingly, for each data partition (e.g., partition #1), the de-grouping module retrieves the corresponding slice grouping from the DST execution units (EU) (e.g., DST 1-5).

As shown, DST execution unit #1 provides a first slice grouping, which includes the first encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 1-15); DST execution unit #2 provides a second slice grouping, which includes the second encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 16-30); DST execution unit #3 provides a third slice grouping, which includes the third encoded slices of each of the sets of encoded slices (e.g., encoded data slices of contiguous data of data blocks 31-45); DST execution unit #4 provides a fourth slice grouping, which includes the fourth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data); and DST execution unit #5 provides a fifth slice grouping, which includes the fifth encoded slices of each of the sets of encoded slices (e.g., first encoded data slices of error coding (EC) data).

The de-grouping module de-groups the slice groupings (e.g., retrieved slices 100) using a de-grouping selector 180 controlled by a control signal 190 as shown in the example to produce a plurality of sets of encoded data slices (e.g., retrieved slices for a partition into sets of slices 122). Each set corresponding to a data segment of the data partition.

FIG. 16 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, a de-segmenting processing module 210, and a control module 186.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186, un-secures each encoded data slice 122 based on slice de-security information received as control information 190 (e.g., the compliment of the slice security information discussed with reference to FIG. 6) received from the control module 186. The slice security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 122, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data 158. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 122 as the sliced encoded data 158 or is bypassed such that the retrieved encoded data slices 122 are provided as the sliced encoded data 158.

The de-slicing module 204 de-slices the sliced encoded data 158 into encoded data segments 156 in accordance with a pillar width of the error correction encoding parameters received as control information 190 from the control module 186. For example, if the pillar width is five, the de-slicing module 204 de-slices a set of five encoded data slices into an encoded data segment 156. The error decoding module 206 decodes the encoded data segments 156 in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments 154. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, unsecures the secured data segments 154 based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module 208 is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment 154, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 154 as the data segment 152 or is bypassed.

The de-segment processing module 210 receives the data segments 152 and receives de-segmenting information as control information 190 from the control module 186. The de-segmenting information indicates how the de-segment processing module 210 is to de-segment the data segments 152 into a data partition 120. For example, the de-segmenting information indicates how the rows and columns of data segments are to be rearranged to yield the data partition 120.

FIG. 17 is a diagram of an example of de-slicing and error decoding processing of a dispersed error decoding module. A de-slicing module 204 receives at least a decode threshold number of encoded data slices 158 for each data segment in accordance with control information 190 and provides encoded data 156. In this example, a decode threshold is three. As such, each set of encoded data slices 158 is shown to have three encoded data slices per data segment. The de-slicing module 204 may receive three encoded data slices per data segment because an associated distributed storage and task (DST) client module requested retrieving only three encoded data slices per segment or selected three of the retrieved encoded data slices per data segment. As shown, which is based on the unity matrix encoding previously discussed with reference to FIG. 8, an encoded data slice may be a data-based encoded data slice (e.g., DS1_d1&d2) or an error code based encoded data slice (e.g., ES3_1).

An error decoding module 206 decodes the encoded data 156 of each data segment in accordance with the error correction decoding parameters of control information 190 to produce secured segments 154. In this example, data segment 1 includes 3 rows with each row being treated as one word for encoding. As such, data segment 1 includes three words: word 1 including data blocks d1 and d2, word 2 including data blocks d16 and d17, and word 3 including data blocks d31 and d32. Each of data segments 2-7 includes three words where each word includes two data blocks. Data segment 8 includes three words where each word includes a single data block (e.g., d15, d30, and d45).

FIG. 18 is a diagram of an example of de-segment processing of an inbound distributed storage and task (DST) processing. In this example, a de-segment processing module 210 receives data segments 152 (e.g., 1-8) and rearranges the data blocks of the data segments into rows and columns in accordance with de-segmenting information of control information 190 to produce a data partition 120. Note that the number of rows is based on the decode threshold (e.g., 3 in this specific example) and the number of columns is based on the number and size of the data blocks.

The de-segmenting module 210 converts the rows and columns of data blocks into the data partition 120. Note that each data block may be of the same size as other data blocks or of a different size. In addition, the size of each data block may be a few bytes to megabytes of data.

FIG. 19 is a diagram of an example of converting slice groups into data 92 within an inbound distributed storage and task (DST) processing section. As shown, the data 92 is reconstructed from a plurality of data partitions (1-x, where x is an integer greater than 4). Each data partition (or chunk set of data) is decoded and re-grouped using a de-grouping and decoding function 212 and a de-partition function 214 from slice groupings as previously discussed. For a given data partition, the slice groupings (e.g., at least a decode threshold per data segment of encoded data slices) are received from DST execution units. From data partition to data partition, the ordering of the slice groupings received from the DST execution units may vary as discussed with reference to FIG. 10.

FIG. 20 is a diagram of an example of a distributed storage and/or retrieval within the distributed computing system. The distributed computing system includes a plurality of distributed storage and/or task (DST) processing client modules 34 (one shown) coupled to a distributed storage and/or task processing network (DSTN) module, or multiple DSTN modules, via a network 24. The DST client module 34 includes an outbound DST processing section 80 and an inbound DST processing section 82. The DSTN module includes a plurality of DST execution units. Each DST execution unit includes a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34.

In an example of data storage, the DST client module 34 has data 92 that it desires to store in the DSTN module. The data 92 may be a file (e.g., video, audio, text, graphics, etc.), a data object, a data block, an update to a file, an update to a data block, etc. In this instance, the outbound DST processing module 80 converts the data 92 into encoded data slices 216 as will be further described with reference to FIGS. 21-23. The outbound DST processing module 80 sends, via the network 24, to the DST execution units for storage as further described with reference to FIG. 24.

In an example of data retrieval, the DST client module 34 issues a retrieve request to the DST execution units for the desired data 92. The retrieve request may address each DST executions units storing encoded data slices of the desired data, address a decode threshold number of DST execution units, address a read threshold number of DST execution units, or address some other number of DST execution units. In response to the request, each addressed DST execution unit retrieves its encoded data slices 100 of the desired data and sends them to the inbound DST processing section 82, via the network 24.

When, for each data segment, the inbound DST processing section 82 receives at least a decode threshold number of encoded data slices 100, it converts the encoded data slices 100 into a data segment. The inbound DST processing section 82 aggregates the data segments to produce the retrieved data 92.

FIG. 21 is a schematic block diagram of an embodiment of an outbound distributed storage and/or task (DST) processing section 80 of a DST client module coupled to a distributed storage and task network (DSTN) module (e.g., a plurality of DST execution units) via a network 24. The outbound DST processing section 80 includes a data partitioning module 110, a dispersed storage (DS) error encoding module 112, a grouping selector module 114, a control module 116, and a distributed task control module 118.

In an example of operation, the data partitioning module 110 is by-passed such that data 92 is provided directly to the DS error encoding module 112. The control module 116 coordinates the by-passing of the data partitioning module 110 by outputting a bypass 220 message to the data partitioning module 110.

The DS error encoding module 112 receives the data 92 in a serial manner, a parallel manner, and/or a combination thereof. The DS error encoding module 112 DS error encodes the data in accordance with control information 160 from the control module 116 to produce encoded data slices 218. The DS error encoding includes segmenting the data 92 into data segments, segment security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)), error encoding, slicing, and/or per slice security processing (e.g., encryption, compression, watermarking, integrity check (e.g., CRC, etc.)). The control information 160 indicates which steps of the DS error encoding are active for the data 92 and, for active steps, indicates the parameters for the step. For example, the control information 160 indicates that the error encoding is active and includes error encoding parameters (e.g., pillar width, decode threshold, write threshold, read threshold, type of error encoding, etc.).

The group selector module 114 groups the encoded slices 218 of the data segments into pillars of slices 216. The number of pillars corresponds to the pillar width of the DS error encoding parameters. In this example, the distributed task control module 118 facilitates the storage request.

FIG. 22 is a schematic block diagram of an example of a dispersed storage (DS) error encoding module 112 for the example of FIG. 21. The DS error encoding module 112 includes a segment processing module 142, a segment security processing module 144, an error encoding module 146, a slicing module 148, and a per slice security processing module 150. Each of these modules is coupled to a control module 116 to receive control information 160 therefrom.

In an example of operation, the segment processing module 142 receives data 92 and receives segmenting information as control information 160 from the control module 116. The segmenting information indicates how the segment processing module is to segment the data. For example, the segmenting information indicates the size of each data segment. The segment processing module 142 segments the data 92 into data segments 152 in accordance with the segmenting information.

The segment security processing module 144, when enabled by the control module 116, secures the data segments 152 based on segment security information received as control information 160 from the control module 116. The segment security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the segment security processing module 144 is enabled, it compresses a data segment 152, encrypts the compressed data segment, and generates a CRC value for the encrypted data segment to produce a secure data segment. When the segment security processing module 144 is not enabled, it passes the data segments 152 to the error encoding module 146 or is bypassed such that the data segments 152 are provided to the error encoding module 146.

The error encoding module 146 encodes the secure data segments in accordance with error correction encoding parameters received as control information 160 from the control module 116. The error correction encoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction encoding parameters identify a specific error correction encoding scheme, specifies a pillar width of five, and specifies a decode threshold of three. From these parameters, the error encoding module 146 encodes a data segment to produce an encoded data segment.

The slicing module 148 slices the encoded data segment in accordance with a pillar width of the error correction encoding parameters. For example, if the pillar width is five, the slicing module slices an encoded data segment into a set of five encoded data slices. As such, for a plurality of data segments, the slicing module 148 outputs a plurality of sets of encoded data slices as shown within encoding and slicing function 222 as described.

The per slice security processing module 150, when enabled by the control module 116, secures each encoded data slice based on slice security information received as control information 160 from the control module 116. The slice security information includes data compression, encryption, watermarking, integrity check (e.g., CRC, etc.), and/or any other type of digital security. For example, when the per slice security processing module 150 is enabled, it may compress an encoded data slice, encrypt the compressed encoded data slice, and generate a CRC value for the encrypted encoded data slice to produce a secure encoded data slice tweaking. When the per slice security processing module 150 is not enabled, it passes the encoded data slices or is bypassed such that the encoded data slices 218 are the output of the DS error encoding module 112.

FIG. 23 is a diagram of an example of converting data 92 into pillar slice groups utilizing encoding, slicing and pillar grouping function 224 for storage in memory of a distributed storage and task network (DSTN) module. As previously discussed the data 92 is encoded and sliced into a plurality of sets of encoded data slices; one set per data segment. The grouping selector module organizes the sets of encoded data slices into pillars of data slices. In this example, the DS error encoding parameters include a pillar width of 5 and a decode threshold of 3. As such, for each data segment, 5 encoded data slices are created.

The grouping selector module takes the first encoded data slice of each of the sets and forms a first pillar, which may be sent to the first DST execution unit. Similarly, the grouping selector module creates the second pillar from the second slices of the sets; the third pillar from the third slices of the sets; the fourth pillar from the fourth slices of the sets; and the fifth pillar from the fifth slices of the set.

FIG. 24 is a schematic block diagram of an embodiment of a distributed storage and/or task (DST) execution unit that includes an interface 169, a controller 86, memory 88, one or more distributed task (DT) execution modules 90, and a DST client module 34. A computing core 26 may be utilized to implement the one or more DT execution modules 90 and the DST client module 34. The memory 88 is of sufficient size to store a significant number of encoded data slices (e.g., thousands of slices to hundreds-of-millions of slices) and may include one or more hard drives and/or one or more solid-state memory devices (e.g., flash memory, DRAM, etc.).

In an example of storing a pillar of slices 216, the DST execution unit receives, via interface 169, a pillar of slices 216 (e.g., pillar #1 slices). The memory 88 stores the encoded data slices 216 of the pillar of slices in accordance with memory control information 174 it receives from the controller 86. The controller 86 (e.g., a processing module, a CPU, etc.) generates the memory control information 174 based on distributed storage information (e.g., user information (e.g., user ID, distributed storage permissions, data access permission, etc.), vault information (e.g., virtual memory assigned to user, user group, etc.), etc.). Similarly, when retrieving slices, the DST execution unit receives, via interface 169, a slice retrieval request. The memory 88 retrieves the slice in accordance with memory control information 174 it receives from the controller 86. The memory 88 outputs the slice 100, via the interface 169, to a requesting entity.

FIG. 25 is a schematic block diagram of an example of operation of an inbound distributed storage and/or task (DST) processing section 82 for retrieving dispersed error encoded data 92. The inbound DST processing section 82 includes a de-grouping module 180, a dispersed storage (DS) error decoding module 182, a data de-partitioning module 184, a control module 186, and a distributed task control module 188. Note that the control module 186 and/or the distributed task control module 188 may be separate modules from corresponding ones of an outbound DST processing section or may be the same modules.

In an example of operation, the inbound DST processing section 82 is retrieving stored data 92 from the DST execution units (i.e., the DSTN module). In this example, the DST execution units output encoded data slices corresponding to data retrieval requests from the distributed task control module 188. The de-grouping module 180 receives pillars of slices 100 and de-groups them in accordance with control information 190 from the control module 186 to produce sets of encoded data slices 218. The DS error decoding module 182 decodes, in accordance with the DS error encoding parameters received as control information 190 from the control module 186, each set of encoded data slices 218 to produce data segments, which are aggregated into retrieved data 92. The data de-partitioning module 184 is by-passed in this operational mode via a bypass signal 226 of control information 190 from the control module 186.

FIG. 26 is a schematic block diagram of an embodiment of a dispersed storage (DS) error decoding module 182 of an inbound distributed storage and task (DST) processing section. The DS error decoding module 182 includes an inverse per slice security processing module 202, a de-slicing module 204, an error decoding module 206, an inverse segment security module 208, and a de-segmenting processing module 210. The dispersed error decoding module 182 is operable to de-slice and decode encoded slices per data segment 218 utilizing a de-slicing and decoding function 228 to produce a plurality of data segments that are de-segmented utilizing a de-segment function 230 to recover data 92.

In an example of operation, the inverse per slice security processing module 202, when enabled by the control module 186 via control information 190, unsecures each encoded data slice 218 based on slice de-security information (e.g., the compliment of the slice security information discussed with reference to FIG. 6) received as control information 190 from a control module 186. The slice de-security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC verification, etc.), and/or any other type of digital security. For example, when the inverse per slice security processing module 202 is enabled, it verifies integrity information (e.g., a CRC value) of each encoded data slice 218, it decrypts each verified encoded data slice, and decompresses each decrypted encoded data slice to produce slice encoded data. When the inverse per slice security processing module 202 is not enabled, it passes the encoded data slices 218 as the sliced encoded data or is bypassed such that the retrieved encoded data slices 218 are provided as the sliced encoded data.

The de-slicing module 204 de-slices the sliced encoded data into encoded data segments in accordance with a pillar width of the error correction encoding parameters received as control information 190 from a control module 186. For example, if the pillar width is five, the de-slicing module de-slices a set of five encoded data slices into an encoded data segment. Alternatively, the encoded data segment may include just three encoded data slices (e.g., when the decode threshold is 3).

The error decoding module 206 decodes the encoded data segments in accordance with error correction decoding parameters received as control information 190 from the control module 186 to produce secure data segments. The error correction decoding parameters include identifying an error correction encoding scheme (e.g., forward error correction algorithm, a Reed-Solomon based algorithm, an information dispersal algorithm, etc.), a pillar width, a decode threshold, a read threshold, a write threshold, etc. For example, the error correction decoding parameters identify a specific error correction encoding scheme, specify a pillar width of five, and specify a decode threshold of three.

The inverse segment security processing module 208, when enabled by the control module 186, un-secures the secured data segments based on segment security information received as control information 190 from the control module 186. The segment security information includes data decompression, decryption, de-watermarking, integrity check (e.g., CRC, etc.) verification, and/or any other type of digital security. For example, when the inverse segment security processing module is enabled, it verifies integrity information (e.g., a CRC value) of each secure data segment, it decrypts each verified secured data segment, and decompresses each decrypted secure data segment to produce a data segment 152. When the inverse segment security processing module 208 is not enabled, it passes the decoded data segment 152 as the data segment or is bypassed. The de-segmenting processing module 210 aggregates the data segments 152 into the data 92 in accordance with control information 190 from the control module 186.

FIG. 27 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module that includes a plurality of distributed storage and task (DST) execution units (#1 through #n, where, for example, n is an integer greater than or equal to three). Each of the DST execution units includes a DST client module 34, a controller 86, one or more DT (distributed task) execution modules 90, and memory 88.

In this example, the DSTN module stores, in the memory of the DST execution units, a plurality of DS (dispersed storage) encoded data (e.g., 1 through n, where n is an integer greater than or equal to two) and stores a plurality of DS encoded task codes (e.g., 1 through k, where k is an integer greater than or equal to two). The DS encoded data may be encoded in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups). The data that is encoded into the DS encoded data may be of any size and/or of any content. For example, the data may be one or more digital books, a copy of a company's emails, a large-scale Internet search, a video security file, one or more entertainment video files (e.g., television programs, movies, etc.), data files, and/or any other large amount of data (e.g., greater than a few Terabytes).

The tasks that are encoded into the DS encoded task code may be a simple function (e.g., a mathematical function, a logic function, an identify function, a find function, a search engine function, a replace function, etc.), a complex function (e.g., compression, human and/or computer language translation, text-to-voice conversion, voice-to-text conversion, etc.), multiple simple and/or complex functions, one or more algorithms, one or more applications, etc. The tasks may be encoded into the DS encoded task code in accordance with one or more examples described with reference to FIGS. 3-19 (e.g., organized in slice groupings) or encoded in accordance with one or more examples described with reference to FIGS. 20-26 (e.g., organized in pillar groups).

In an example of operation, a DST client module of a user device or of a DST processing unit issues a DST request to the DSTN module. The DST request may include a request to retrieve stored data, or a portion thereof, may include a request to store data that is included with the DST request, may include a request to perform one or more tasks on stored data, may include a request to perform one or more tasks on data included with the DST request, etc. In the cases where the DST request includes a request to store data or to retrieve data, the client module and/or the DSTN module processes the request as previously discussed with reference to one or more of FIGS. 3-19 (e.g., slice groupings) and/or 20-26 (e.g., pillar groupings). In the case where the DST request includes a request to perform one or more tasks on data included with the DST request, the DST client module and/or the DSTN module process the DST request as previously discussed with reference to one or more of FIGS. 3-19.

In the case where the DST request includes a request to perform one or more tasks on stored data, the DST client module and/or the DSTN module processes the DST request as will be described with reference to one or more of FIGS. 28-39. In general, the DST client module identifies data and one or more tasks for the DSTN module to execute upon the identified data. The DST request may be for a one-time execution of the task or for an on-going execution of the task. As an example of the latter, as a company generates daily emails, the DST request may be to daily search new emails for inappropriate content and, if found, record the content, the email sender(s), the email recipient(s), email routing information, notify human resources of the identified email, etc.

FIG. 28 is a schematic block diagram of an example of a distributed computing system performing tasks on stored data. In this example, two distributed storage and task (DST) client modules 1-2 are shown: the first may be associated with a user device and the second may be associated with a DST processing unit or a high priority user device (e.g., high priority clearance user, system administrator, etc.). Each DST client module includes a list of stored data 234 and a list of tasks codes 236. The list of stored data 234 includes one or more entries of data identifying information, where each entry identifies data stored in the DSTN module 22. The data identifying information (e.g., data ID) includes one or more of a data file name, a data file directory listing, DSTN addressing information of the data, a data object identifier, etc. The list of tasks 236 includes one or more entries of task code identifying information, when each entry identifies task codes stored in the DSTN module 22. The task code identifying information (e.g., task ID) includes one or more of a task file name, a task file directory listing, DSTN addressing information of the task, another type of identifier to identify the task, etc.

As shown, the list of data 234 and the list of tasks 236 are each smaller in number of entries for the first DST client module than the corresponding lists of the second DST client module. This may occur because the user device associated with the first DST client module has fewer privileges in the distributed computing system than the device associated with the second DST client module. Alternatively, this may occur because the user device associated with the first DST client module serves fewer users than the device associated with the second DST client module and is restricted by the distributed computing system accordingly. As yet another alternative, this may occur through no restraints by the distributed computing system, it just occurred because the operator of the user device associated with the first DST client module has selected fewer data and/or fewer tasks than the operator of the device associated with the second DST client module.

In an example of operation, the first DST client module selects one or more data entries 238 and one or more tasks 240 from its respective lists (e.g., selected data ID and selected task ID). The first DST client module sends its selections to a task distribution module 232. The task distribution module 232 may be within a stand-alone device of the distributed computing system, may be within the user device that contains the first DST client module, or may be within the DSTN module 22.

Regardless of the task distribution module's location, it generates DST allocation information 242 from the selected task ID 240 and the selected data ID 238. The DST allocation information 242 includes data partitioning information, task execution information, and/or intermediate result information. The task distribution module 232 sends the DST allocation information 242 to the DSTN module 22. Note that one or more examples of the DST allocation information will be discussed with reference to one or more of FIGS. 29-39.

The DSTN module 22 interprets the DST allocation information 242 to identify the stored DS encoded data (e.g., DS error encoded data 2) and to identify the stored DS error encoded task code (e.g., DS error encoded task code 1). In addition, the DSTN module 22 interprets the DST allocation information 242 to determine how the data is to be partitioned and how the task is to be partitioned. The DSTN module 22 also determines whether the selected DS error encoded data 238 needs to be converted from pillar grouping to slice grouping. If so, the DSTN module 22 converts the selected DS error encoded data into slice groupings and stores the slice grouping DS error encoded data by overwriting the pillar grouping DS error encoded data or by storing it in a different location in the memory of the DSTN module 22 (i.e., does not overwrite the pillar grouping DS encoded data).

The DSTN module 22 partitions the data and the task as indicated in the DST allocation information 242 and sends the portions to selected DST execution units of the DSTN module 22. Each of the selected DST execution units performs its partial task(s) on its slice groupings to produce partial results. The DSTN module 22 collects the partial results from the selected DST execution units and provides them, as result information 244, to the task distribution module. The result information 244 may be the collected partial results, one or more final results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242, or one or more intermediate results as produced by the DSTN module 22 from processing the partial results in accordance with the DST allocation information 242.

The task distribution module 232 receives the result information 244 and provides one or more final results 104 therefrom to the first DST client module. The final result(s) 104 may be result information 244 or a result(s) of the task distribution module's processing of the result information 244.

In concurrence with processing the selected task of the first DST client module, the distributed computing system may process the selected task(s) of the second DST client module on the selected data(s) of the second DST client module. Alternatively, the distributed computing system may process the second DST client module's request subsequent to, or preceding, that of the first DST client module. Regardless of the ordering and/or parallel processing of the DST client module requests, the second DST client module provides its selected data 238 and selected task 240 to a task distribution module 232. If the task distribution module 232 is a separate device of the distributed computing system or within the DSTN module, the task distribution modules 232 coupled to the first and second DST client modules may be the same module. The task distribution module 232 processes the request of the second DST client module in a similar manner as it processed the request of the first DST client module.

FIG. 29 is a schematic block diagram of an embodiment of a task distribution module 232 facilitating the example of FIG. 28. The task distribution module 232 includes a plurality of tables it uses to generate distributed storage and task (DST) allocation information 242 for selected data and selected tasks received from a DST client module. The tables include data storage information 248, task storage information 250, distributed task (DT) execution module information 252, and task⇔sub-task mapping information 246.

The data storage information table 248 includes a data identification (ID) field 260, a data size field 262, an addressing information field 264, distributed storage (DS) information 266, and may further include other information regarding the data, how it is stored, and/or how it can be processed. For example, DS encoded data #1 has a data ID of 1, a data size of AA (e.g., a byte size of a few Terabytes or more), addressing information of Addr_1_AA, and DS parameters of 3/5; SEG_1; and SLC_1. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the data and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the data, physical addresses of the first storage word or the storage words of the data, may be a list of slice names of the encoded data slices of the data, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_1), per slice security information (e.g., SLC_1), and/or any other information regarding how the data was encoded into data slices.

The task storage information table 250 includes a task identification (ID) field 268, a task size field 270, an addressing information field 272, distributed storage (DS) information 274, and may further include other information regarding the task, how it is stored, and/or how it can be used to process data. For example, DS encoded task #2 has a task ID of 2, a task size of XY, addressing information of Addr_2_XY, and DS parameters of 3/5; SEG_2; and SLC_2. In this example, the addressing information may be a virtual address corresponding to the virtual address of the first storage word (e.g., one or more bytes) of the task and information on how to calculate the other addresses, may be a range of virtual addresses for the storage words of the task, physical addresses of the first storage word or the storage words of the task, may be a list of slice names of the encoded slices of the task code, etc. The DS parameters may include identity of an error encoding scheme, decode threshold/pillar width (e.g., 3/5 for the first data entry), segment security information (e.g., SEG_2), per slice security information (e.g., SLC_2), and/or any other information regarding how the task was encoded into encoded task slices. Note that the segment and/or the per-slice security information include a type of encryption (if enabled), a type of compression (if enabled), watermarking information (if enabled), and/or an integrity check scheme (if enabled).

The task⇔sub-task mapping information table 246 includes a task field 256 and a sub-task field 258. The task field 256 identifies a task stored in the memory of a distributed storage and task network (DSTN) module and the corresponding sub-task fields 258 indicates whether the task includes sub-tasks and, if so, how many and if any of the sub-tasks are ordered. In this example, the task⇔sub-task mapping information table 246 includes an entry for each task stored in memory of the DSTN module (e.g., task 1 through task k). In particular, this example indicates that task 1 includes 7 sub-tasks; task 2 does not include sub-tasks, and task k includes r number of sub-tasks (where r is an integer greater than or equal to two).

The DT execution module table 252 includes a DST execution unit ID field 276, a DT execution module ID field 278, and a DT execution module capabilities field 280. The DST execution unit ID field 276 includes the identity of DST units in the DSTN module. The DT execution module ID field 278 includes the identity of each DT execution unit in each DST unit. For example, DST unit 1 includes three DT executions modules (e.g., 1_1, 1_2, and 1_3). The DT execution capabilities field 280 includes identity of the capabilities of the corresponding DT execution unit. For example, DT execution module 1_1 includes capabilities X, where X includes one or more of MIPS capabilities, processing resources (e.g., quantity and capability of microprocessors, CPUs, digital signal processors, co-processor, microcontrollers, arithmetic logic circuitry, and/or any other analog and/or digital processing circuitry), availability of the processing resources, memory information (e.g., type, size, availability, etc.), and/or any information germane to executing one or more tasks.

From these tables, the task distribution module 232 generates the DST allocation information 242 to indicate where the data is stored, how to partition the data, where the task is stored, how to partition the task, which DT execution units should perform which partial task on which data partitions, where and how intermediate results are to be stored, etc. If multiple tasks are being performed on the same data or different data, the task distribution module factors such information into its generation of the DST allocation information.

FIG. 30 is a diagram of a specific example of a distributed computing system performing tasks on stored data as a task flow 318. In this example, selected data 92 is data 2 and selected tasks are tasks 1, 2, and 3. Task 1 corresponds to analyzing translation of data from one language to another (e.g., human language or computer language); task 2 corresponds to finding specific words and/or phrases in the data; and task 3 corresponds to finding specific translated words and/or phrases in translated data.

In this example, task 1 includes 7 sub-tasks: task 1_1—identify non-words (non-ordered); task 1_2—identify unique words (non-ordered); task 1_3—translate (non-ordered); task 1_4—translate back (ordered after task 1_3); task 1_5—compare to ID errors (ordered after task 1-4); task 1_6—determine non-word translation errors (ordered after task 1_5 and 1_1); and task 1_7—determine correct translations (ordered after 1_5 and 1_2). The sub-task further indicates whether they are an ordered task (i.e., are dependent on the outcome of another task) or non-order (i.e., are independent of the outcome of another task). Task 2 does not include sub-tasks and task 3 includes two sub-tasks: task 3_1 translate; and task 3_2 find specific word or phrase in translated data.

In general, the three tasks collectively are selected to analyze data for translation accuracies, translation errors, translation anomalies, occurrence of specific words or phrases in the data, and occurrence of specific words or phrases on the translated data. Graphically, the data 92 is translated 306 into translated data 282; is analyzed for specific words and/or phrases 300 to produce a list of specific words and/or phrases 286; is analyzed for non-words 302 (e.g., not in a reference dictionary) to produce a list of non-words 290; and is analyzed for unique words 316 included in the data 92 (i.e., how many different words are included in the data) to produce a list of unique words 298. Each of these tasks is independent of each other and can therefore be processed in parallel if desired.

The translated data 282 is analyzed (e.g., sub-task 3_2) for specific translated words and/or phrases 304 to produce a list of specific translated words and/or phrases 288. The translated data 282 is translated back 308 (e.g., sub-task 1_4) into the language of the original data to produce re-translated data 284. These two tasks are dependent on the translate task (e.g., task 1_3) and thus must be ordered after the translation task, which may be in a pipelined ordering or a serial ordering. The re-translated data 284 is then compared 310 with the original data 92 to find words and/or phrases that did not translate (one way and/or the other) properly to produce a list of incorrectly translated words 294. As such, the comparing task (e.g., sub-task 1_5) 310 is ordered after the translation 306 and re-translation tasks 308 (e.g., sub-tasks 1_3 and 1_4).

The list of words incorrectly translated 294 is compared 312 to the list of non-words 290 to identify words that were not properly translated because the words are non-words to produce a list of errors due to non-words 292. In addition, the list of words incorrectly translated 294 is compared 314 to the list of unique words 298 to identify unique words that were properly translated to produce a list of correctly translated words 296. The comparison may also identify unique words that were not properly translated to produce a list of unique words that were not properly translated. Note that each list of words (e.g., specific words and/or phrases, non-words, unique words, translated words and/or phrases, etc.) may include the word and/or phrase, how many times it is used, where in the data it is used, and/or any other information requested regarding a word and/or phrase.

FIG. 31 is a schematic block diagram of an example of a distributed storage and task processing network (DSTN) module storing data and task codes for the example of FIG. 30. As shown, DS encoded data 2 is stored as encoded data slices across the memory (e.g., stored in memories 88) of DST execution units 1-5; the DS encoded task code 1 (of task 1) and DS encoded task 3 are stored as encoded task slices across the memory of DST execution units 1-5; and DS encoded task code 2 (of task 2) is stored as encoded task slices across the memory of DST execution units 3-7. As indicated in the data storage information table and the task storage information table of FIG. 29, the respective data/task has DS parameters of 3/5 for their decode threshold/pillar width; hence spanning the memory of five DST execution units.

FIG. 32 is a diagram of an example of distributed storage and task (DST) allocation information 242 for the example of FIG. 30. The DST allocation information 242 includes data partitioning information 320, task execution information 322, and intermediate result information 324. The data partitioning information 320 includes the data identifier (ID), the number of partitions to split the data into, address information for each data partition, and whether the DS encoded data has to be transformed from pillar grouping to slice grouping. The task execution information 322 includes tabular information having a task identification field 326, a task ordering field 328, a data partition field ID 330, and a set of DT execution modules 332 to use for the distributed task processing per data partition. The intermediate result information 324 includes tabular information having a name ID field 334, an ID of the DST execution unit assigned to process the corresponding intermediate result 336, a scratch pad storage field 338, and an intermediate result storage field 340.

Continuing with the example of FIG. 30, where tasks 1-3 are to be distributedly performed on data 2, the data partitioning information includes the ID of data 2. In addition, the task distribution module determines whether the DS encoded data 2 is in the proper format for distributed computing (e.g., was stored as slice groupings). If not, the task distribution module indicates that the DS encoded data 2 format needs to be changed from the pillar grouping format to the slice grouping format, which will be done by the DSTN module. In addition, the task distribution module determines the number of partitions to divide the data into (e.g., 2_1 through 2_z) and addressing information for each partition.

The task distribution module generates an entry in the task execution information section for each sub-task to be performed. For example, task 1_1 (e.g., identify non-words on the data) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1. For instance, DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 search for non-words in data partitions 2_1 through 2_z to produce task 1_1 intermediate results (R1-1, which is a list of non-words). Task 1_2 (e.g., identify unique words) has similar task execution information as task 1_1 to produce task 1_2 intermediate results (R1-2, which is the list of unique words).

Task 1_3 (e.g., translate) includes task execution information as being non-ordered (i.e., is independent), having DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and having DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z to produce task 1_3 intermediate results (R1-3, which is the translated data). In this example, the data partitions are grouped, where different sets of DT execution modules perform a distributed sub-task (or task) on each data partition group, which allows for further parallel processing.

Task 1_4 (e.g., translate back) is ordered after task 1_3 and is to be executed on task 1_3's intermediate result (e.g., R1-3_1) (e.g., the translated data). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to translate back task 1_3 intermediate result partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back task 1_3 intermediate result partitions R1-3_5 through R1-3_z to produce task 1-4 intermediate results (R1-4, which is the translated back data).

Task 1_5 (e.g., compare data and translated data to identify translation errors) is ordered after task 1_4 and is to be executed on task 1_4's intermediate results (R4-1) and on the data. DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the data partitions (2_1 through 2_z) with partitions of task 1-4 intermediate results partitions R1-4_1 through R1-4_z to produce task 1_5 intermediate results (R1-5, which is the list words translated incorrectly).

Task 1_6 (e.g., determine non-word translation errors) is ordered after tasks 1_1 and 1_5 and is to be executed on tasks 1_1's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to compare the partitions of task 1_1 intermediate results (R1-1_1 through R1-1_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_6 intermediate results (R1-6, which is the list translation errors due to non-words).

Task 1_7 (e.g., determine words correctly translated) is ordered after tasks 1_2 and 1_5 and is to be executed on tasks 1_2's and 1_5's intermediate results (R1-1 and R1-5). DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 are allocated to compare the partitions of task 1_2 intermediate results (R1-2_1 through R1-2_z) with partitions of task 1-5 intermediate results partitions (R1-5_1 through R1-5_z) to produce task 1_7 intermediate results (R1-7, which is the list of correctly translated words).

Task 2 (e.g., find specific words and/or phrases) has no task ordering (i.e., is independent of the results of other sub-tasks), is to be performed on data partitions 2_1 through 2_z by DT execution modules 3_1, 4_1, 5_1, 6_1, and 7_1. For instance, DT execution modules 3_1, 4_1, 5_1, 6_1, and 7_1 search for specific words and/or phrases in data partitions 2_1 through 2_z to produce task 2 intermediate results (R2, which is a list of specific words and/or phrases).

Task 3_2 (e.g., find specific translated words and/or phrases) is ordered after task 1_3 (e.g., translate) is to be performed on partitions R1-3_1 through R1-3_z by DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2. For instance, DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 search for specific translated words and/or phrases in the partitions of the translated data (R1-3_1 through R1-3_z) to produce task 3_2 intermediate results (R3-2, which is a list of specific translated words and/or phrases).

For each task, the intermediate result information indicates which DST unit is responsible for overseeing execution of the task and, if needed, processing the partial results generated by the set of allocated DT execution units. In addition, the intermediate result information indicates a scratch pad memory for the task and where the corresponding intermediate results are to be stored. For example, for intermediate result R1-1 (the intermediate result of task 1_1), DST unit 1 is responsible for overseeing execution of the task 1_1 and coordinates storage of the intermediate result as encoded intermediate result slices stored in memory of DST execution units 1-5. In general, the scratch pad is for storing non-DS encoded intermediate results and the intermediate result storage is for storing DS encoded intermediate results.

FIGS. 33-38 are schematic block diagrams of the distributed storage and task network (DSTN) module performing the example of FIG. 30. In FIG. 33, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with distributed storage and task network (DST) allocation information. For each data partition, the DSTN identifies a set of its DT (distributed task) execution modules 90 to perform the task (e.g., identify non-words (i.e., not in a reference dictionary) within the data partition) in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules 90 may be the same, different, or a combination thereof (e.g., some data partitions use the same set while other data partitions use different sets).

For the first data partition, the first set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32) executes task 1_1 to produce a first partial result 102 of non-words found in the first data partition. The second set of DT execution modules (e.g., 1_1, 2_1, 3_1, 4_1, and 5_1 per the DST allocation information of FIG. 32) executes task 1_1 to produce a second partial result 102 of non-words found in the second data partition. The sets of DT execution modules (as per the DST allocation information) perform task 1_1 on the data partitions until the “z” set of DT execution modules performs task 1_1 on the “zth” data partition to produce a “zth” partial result 102 of non-words found in the “zth” data partition.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results to produce the first intermediate result (R1-1), which is a list of non-words found in the data. For instance, each set of DT execution modules 90 stores its respective partial result in the scratchpad memory of DST execution unit 1 (which is identified in the DST allocation or may be determined by DST execution unit 1). A processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results to produce the first intermediate result (e.g., R1_1). The processing module stores the first intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the first intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of non-words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the first intermediate result (R1-1) into a plurality of partitions (e.g., R1-1_1 through R1-1_m). If the first intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the first intermediate result, or for the first intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 34, the DSTN module is performing task 1_2 (e.g., find unique words) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 1_2 to produce a partial results (e.g., 1^(st) through “zth”) of unique words found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results 102 of task 1_2 to produce the second intermediate result (R1-2), which is a list of unique words found in the data 92. The processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of unique words to produce the second intermediate result. The processing module stores the second intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the second intermediate result (e.g., the list of non-words). To begin the encoding, the DST client module determines whether the list of unique words is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the second intermediate result (R1-2) into a plurality of partitions (e.g., R1-2_1 through R1-2_m). If the second intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the second intermediate result, or for the second intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5).

In FIG. 35, the DSTN module is performing task 1_3 (e.g., translate) on the data 92. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules to perform task 1_3 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 translate data partitions 2_1 through 2_4 and DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2 translate data partitions 2_5 through 2_z). For the data partitions, the allocated set of DT execution modules 90 executes task 1_3 to produce partial results 102 (e.g., 1^(st) through “zth”) of translated data.

As indicated in the DST allocation information of FIG. 32, DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_3 to produce the third intermediate result (R1-3), which is translated data. The processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of translated data to produce the third intermediate result. The processing module stores the third intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the third intermediate result (e.g., translated data). To begin the encoding, the DST client module partitions the third intermediate result (R1-3) into a plurality of partitions (e.g., R1-3_1 through R1-3_y). For each partition of the third intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is further shown in FIG. 35, the DSTN module is performing task 1_4 (e.g., retranslate) on the translated data of the third intermediate result. To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition of the third intermediate result, the DSTN identifies a set of its DT execution modules 90 to perform task 1_4 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1 are allocated to translate back partitions R1-3_1 through R1-3_4 and DT execution modules 1_2, 2_2, 6_1, 7_1, and 7_2 are allocated to translate back partitions R1-3_5 through R1-3_z). For the partitions, the allocated set of DT execution modules executes task 1_4 to produce partial results 102 (e.g., 1^(st) through “zth”) of re-translated data.

As indicated in the DST allocation information of FIG. 32, DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_4 to produce the fourth intermediate result (R1-4), which is retranslated data. The processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of retranslated data to produce the fourth intermediate result. The processing module stores the fourth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the fourth intermediate result (e.g., retranslated data). To begin the encoding, the DST client module partitions the fourth intermediate result (R1-4) into a plurality of partitions (e.g., R1-4_1 through R1-4_z). For each partition of the fourth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 36, a distributed storage and task network (DSTN) module is performing task 1_5 (e.g., compare) on data 92 and retranslated data of FIG. 35. To begin, the DSTN module accesses the data 92 and partitions it into a plurality of partitions in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. The DSTN module also accesses the retranslated data from the scratchpad memory, or from the intermediate result memory and decodes it, and partitions it into a plurality of partitions in accordance with the DST allocation information. The number of partitions of the retranslated data corresponds to the number of partitions of the data.

For each pair of partitions (e.g., data partition 1 and retranslated data partition 1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_5 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_5 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST execution unit 1 is assigned to process the first through “zth” partial results of task 1_5 to produce the fifth intermediate result (R1-5), which is the list of incorrectly translated words and/or phrases. In particular, the processing module of DST execution 1 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases to produce the fifth intermediate result. The processing module stores the fifth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 1.

DST execution unit 1 engages its DST client module to slice grouping based DS error encode the fifth intermediate result. To begin the encoding, the DST client module partitions the fifth intermediate result (R1-5) into a plurality of partitions (e.g., R1-5_1 through R1-5_z). For each partition of the fifth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-5 per the DST allocation information).

As is further shown in FIG. 36, the DSTN module is performing task 1_6 (e.g., translation errors due to non-words) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of non-words (e.g., the first intermediate result R1-1). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-1_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_6 in accordance with the DST allocation information (e.g., DT execution modules 1_1, 2_1, 3_1, 4_1, and 5_1). For each pair of partitions, the allocated set of DT execution modules executes task 1_6 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of incorrectly translated words and/or phrases due to non-words.

As indicated in the DST allocation information of FIG. 32, DST execution unit 2 is assigned to process the first through “zth” partial results of task 1_6 to produce the sixth intermediate result (R1-6), which is the list of incorrectly translated words and/or phrases due to non-words. In particular, the processing module of DST execution 2 is engaged to aggregate the first through “zth” partial results of the list of incorrectly translated words and/or phrases due to non-words to produce the sixth intermediate result. The processing module stores the sixth intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 2.

DST execution unit 2 engages its DST client module to slice grouping based DS error encode the sixth intermediate result. To begin the encoding, the DST client module partitions the sixth intermediate result (R1-6) into a plurality of partitions (e.g., R1-6_1 through R1-6_z). For each partition of the sixth intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 2-6 per the DST allocation information).

As is still further shown in FIG. 36, the DSTN module is performing task 1_7 (e.g., correctly translated words and/or phrases) on the list of incorrectly translated words and/or phrases (e.g., the fifth intermediate result R1-5) and the list of unique words (e.g., the second intermediate result R1-2). To begin, the DSTN module accesses the lists and partitions them into a corresponding number of partitions.

For each pair of partitions (e.g., partition R1-2_1 and partition R1-5_1), the DSTN identifies a set of its DT execution modules 90 to perform task 1_7 in accordance with the DST allocation information (e.g., DT execution modules 1_2, 2_2, 3_2, 4_2, and 5_2). For each pair of partitions, the allocated set of DT execution modules executes task 1_7 to produce partial results 102 (e.g., 1^(st) through “zth”) of a list of correctly translated words and/or phrases.

As indicated in the DST allocation information of FIG. 32, DST execution unit 3 is assigned to process the first through “zth” partial results of task 1_7 to produce the seventh intermediate result (R1-7), which is the list of correctly translated words and/or phrases. In particular, the processing module of DST execution 3 is engaged to aggregate the first through “zth” partial results of the list of correctly translated words and/or phrases to produce the seventh intermediate result. The processing module stores the seventh intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 3.

DST execution unit 3 engages its DST client module to slice grouping based DS error encode the seventh intermediate result. To begin the encoding, the DST client module partitions the seventh intermediate result (R1-7) into a plurality of partitions (e.g., R1-7_1 through R1-7_z). For each partition of the seventh intermediate result, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 3-7 per the DST allocation information).

In FIG. 37, the distributed storage and task network (DSTN) module is performing task 2 (e.g., find specific words and/or phrases) on the data 92. To begin, the DSTN module accesses the data and partitions it into a plurality of partitions 1-z in accordance with the DST allocation information or it may use the data partitions of task 1_1 if the partitioning is the same. For each data partition, the DSTN identifies a set of its DT execution modules 90 to perform task 2 in accordance with the DST allocation information. From data partition to data partition, the set of DT execution modules may be the same, different, or a combination thereof. For the data partitions, the allocated set of DT execution modules executes task 2 to produce partial results 102 (e.g., 1^(st) through “zth”) of specific words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 7 is assigned to process the first through “zth” partial results of task 2 to produce task 2 intermediate result (R2), which is a list of specific words and/or phrases found in the data. The processing module of DST execution 7 is engaged to aggregate the first through “zth” partial results of specific words and/or phrases to produce the task 2 intermediate result. The processing module stores the task 2 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 7 engages its DST client module to slice grouping based DS error encode the task 2 intermediate result. To begin the encoding, the DST client module determines whether the list of specific words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 2 intermediate result (R2) into a plurality of partitions (e.g., R2_1 through R2_m). If the task 2 intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the task 2 intermediate result, or for the task 2 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, and 7).

In FIG. 38, the distributed storage and task network (DSTN) module is performing task 3 (e.g., find specific translated words and/or phrases) on the translated data (R1-3). To begin, the DSTN module accesses the translated data (from the scratchpad memory or from the intermediate result memory and decodes it) and partitions it into a plurality of partitions in accordance with the DST allocation information. For each partition, the DSTN identifies a set of its DT execution modules to perform task 3 in accordance with the DST allocation information. From partition to partition, the set of DT execution modules may be the same, different, or a combination thereof. For the partitions, the allocated set of DT execution modules 90 executes task 3 to produce partial results 102 (e.g., 1^(st) through “zth”) of specific translated words and/or phrases found in the data partitions.

As indicated in the DST allocation information of FIG. 32, DST execution unit 5 is assigned to process the first through “zth” partial results of task 3 to produce task 3 intermediate result (R3), which is a list of specific translated words and/or phrases found in the translated data. In particular, the processing module of DST execution 5 is engaged to aggregate the first through “zth” partial results of specific translated words and/or phrases to produce the task 3 intermediate result. The processing module stores the task 3 intermediate result as non-DS error encoded data in the scratchpad memory or in another section of memory of DST execution unit 7.

DST execution unit 5 engages its DST client module to slice grouping based DS error encode the task 3 intermediate result. To begin the encoding, the DST client module determines whether the list of specific translated words and/or phrases is of a sufficient size to partition (e.g., greater than a Terabyte). If yes, it partitions the task 3 intermediate result (R3) into a plurality of partitions (e.g., R3_1 through R3_m). If the task 3 intermediate result is not of sufficient size to partition, it is not partitioned.

For each partition of the task 3 intermediate result, or for the task 3 intermediate results, the DST client module uses the DS error encoding parameters of the data (e.g., DS parameters of data 2, which includes 3/5 decode threshold/pillar width ratio) to produce slice groupings. The slice groupings are stored in the intermediate result memory (e.g., allocated memory in the memories of DST execution units 1-4, 5, and 7).

FIG. 39 is a diagram of an example of combining result information into final results 104 for the example of FIG. 30. In this example, the result information includes the list of specific words and/or phrases found in the data (task 2 intermediate result), the list of specific translated words and/or phrases found in the data (task 3 intermediate result), the list of non-words found in the data (task 1 first intermediate result R1-1), the list of unique words found in the data (task 1 second intermediate result R1-2), the list of translation errors due to non-words (task 1 sixth intermediate result R1-6), and the list of correctly translated words and/or phrases (task 1 seventh intermediate result R1-7). The task distribution module provides the result information to the requesting DST client module as the results 104.

FIG. 40A is a schematic block diagram of another embodiment of a distributed computing system that includes a data processor 350, a distributed storage and task (DST) client module 34, and a distributed storage and task network (DSTN) module 22. The DSTN module 22 includes a plurality of DST execution (EX) units 36. The data processor 350 and/or the DST client module 34 may be implemented in one or more of a computing device, a server, a user device, a DST processing unit 16, and a DST execution and 36. The data processor 350 and the DST client module 34 are operable to encode data 352 into primary slices 356 (e.g., first encoded data slices) and secondary slices 358 (e.g., second encoded data slices) for storage in the DSTN module 22.

The DST client module 34 encodes the data 352 utilizing a dispersed storage error coding function in accordance with a first set (e.g., temporary) of dispersed storage error coding function parameters to produce one or more sets of primary slices. The DST client module 34 stores the one or more sets of primary slices 356 in the DSTN module 22 (e.g., in a temporary memory portion of the DSTN module 22). The data processor 350 processes the data 352 in accordance with a processing function to produce processed data 354. The processing function includes at least one of a picture size reduction function, a picture resolution reduction function, a lower resolution encoding function, a compression algorithm, an expansion algorithm, and an encoding function. For example, the data processor 350 encodes the data 352 utilizing an encoding function that produces the processed data 354 such that a size of the processed data 354 is less than a size of the data 352.

The DST client module 34 encodes the processed data 354 utilizing the dispersed storage error coding function in accordance with a second set (e.g., non-temporary) of dispersed storage error coding function parameters to produce one or more sets of secondary slices 358. For example, the DST client module 34 selects the second set of dispersed storage error coding function parameters such that a resulting reliability level of storage of secondary slices 358 is lower than a resulting reliability level of storage of the primary slices 356. The DST client module 34 stores the one or more sets of secondary slices 358 of the DSTN module 22 (e.g., in a non-temporary memory portion of the DSTN module 22). The storing includes selecting a set of DST execution units 36 in accordance with a storage goal. For example, the DST client module 34 selects a first set of DST execution units 36 associated with a lower than average reliability level when the storage goal indicates lower reliability is allowable.

The DST client module 34 rebuilds the secondary slices 358 when a slice error is detected of the one or more secondary slices 358. The rebuilding may include prioritization of steps of a rebuilding process in accordance with the storage goal. For example, the DST client module 34 rebuilds a secondary slice 358 with a de-prioritized rebuilding schedule when the storage goal indicates that the prioritized rebuilding is allowable.

The DST client module 34 may determine to delete one or more of the one or more sets of primary slices 356 when deletion is indicated. The indication of deletion includes at least one of receiving a delete request, determining that the one or more primary slices are no longer required, and a time period of use has expired. When the DST client module 34 deletes the one or more primary slices 356, the DST client module 34 determines whether to re-store the processed data 354. The determining may be based on one or more of storage goal, a re-store request, and a predetermination. When the DST client module 34 determines to restore the processed data 354, the DST client module 34 retrieves at least one of the one more sets of secondary slices 358 and the one or more sets of primary slices 356 and decodes retrieved slices to reproduce one or more of the processed data 354 and the data 352.

The DST client module 34 encodes one or more of the processed data 354 and the data 352 utilizing the dispersed storage error coding function in accordance with modified dispersed storage error coding function parameters to produce one or more sets of modified secondary slices. The encoding includes determining the modified dispersed storage error coding function parameters. The determining includes one or more of receiving, retrieving, and generating based on the storage goal. For example, the DST client module 34 determines the modified dispersed storage error coding function parameters to result in improved reliability of storage of the one or more sets of modified secondary slices when the storage goal indicates to re-store the processed data 354 with improved reliability when the one or more primary slices 356 have been deleted. The DST client module 34 stores the one or more sets of modified secondary slices in the DSTN module 22. The storing includes selecting a set of DST execution units 36 in accordance with a storage goal for modified secondary slices.

FIG. 40B is a schematic block diagram of an embodiment of a dispersed storage network system that includes a computing device 360, a dispersed storage network (DSN) 362, and a plurality of sources 382. A source 382 of the plurality of sources 382 may include a communication system, a computing system, and a storage system. Each of the plurality of sources 382 sources data 384. The DSN 362 includes a plurality of storage devices 368 for storage of the data 384. A subset of the plurality of storage devices 368 may be organized into at least one temporary memory 364 to facilitate storage of the data 384 on a temporary temporal basis. Another subset of the plurality of storage devices 368 may be organized into at least one non-temporary memory 366 to facilitate storage of the data 384 on a non-temporary temporal basis. Each storage device 368 of the plurality of storage devices 368 may be implemented utilizing at least one of a storage server, a storage unit, a storage module, a memory device, a memory, a distributed storage (DS) unit, a DS processing unit, a distributed storage and task (DST) execution unit, a user device, a DST processing unit, and a DST processing module.

The computing device 360 includes a dispersed storage (DS) module 370 and a memory 372. The memory 372 may be implemented using one or more of a solid state memory device, an optical disk memory device, and a magnetic disk memory device. The memory 372 may be organized to include a queue 374 for storage of the data 384. For example, an array of solid state memory devices provides the queue 374 to store the data 384. The computing device 360 may be implemented utilizing at least one of a server, a storage unit, a distributed storage and task network (DSTN) managing unit, a DSN managing unit, a DS unit, the storage device 368, a storage server, a storage module, a DS processing unit, a DST execution unit, a user device, a DST processing unit, and a DST processing module. For example, computing device 360 is implemented as the DS processing unit. The DS module 370 includes a queue module 376, a temporary storage module 378, and a storage module 380.

The system functions to receive the data 384 from the plurality of sources 382, temporarily store the data 384 in the DSN 362, and non-temporarily store the data 384 in the DSN 362. With regards to the receiving the data 384 from the plurality of sources 382, the queue module 376 receives the data 384 from the plurality of sources 382 and queues the data 384 for storage in the DSN. For example, the queue module 376 stores the data 384 in the queue 374 of the memory 372.

With regards to the temporarily storing the data 384 in the DSN 362, the temporary storage module 378 utilizes temporary dispersed storage error encoding parameters 390 for efficient and reliable error encoded temporary storage of the data in the DSN. The temporary storage module 378 stores the data 384 in the DSN 362 by performing a series of temporary storage steps. In a first temporary storage step, the temporary storage module 378 determines a loading of the queue 374 corresponding to the queuing of the data 384. In a second temporary storage step, the temporary storage module 378 determines a desired reliability-duration of the temporary storage based on the loading (e.g., minimal risk of losing data within a given period of time). The desired reliability-duration may be established such that it is extremely unlikely that enough storage failures will occur within the given period of time to cause loss of data, thus providing a same level of reliability as using dispersed storage error encoding parameters associated with a higher level of reliability, but for a short period of time.

In a third temporary storage step, the temporary storage module 378 determines the temporary dispersed storage error encoding parameters 390 based on the loading and the desired reliability-duration. For example, the temporary storage module 378 determines the temporary dispersed storage error encoding parameters 390 associated with lower than average retrieval reliability when queue loading (e.g., utilization) is above a high threshold level. In a fourth temporary storage step, the temporary storage module 378 retrieves a data object 386 (e.g., data block, data file, streaming video portion, etc.) of the data 384 from the queue 374. In a fifth temporary storage step, the temporary storage module 378 encodes the retrieved data object 386 of the data 384 in accordance with the temporary dispersed storage error encoding parameters 390 to produce first encoded data slices 388. In a sixth temporary storage step, the temporary storage module 378 stores the first encoded data slices 388 in the temporary memory 364 of the DSN 362. In a seventh temporary storage step, the temporary storage module 378 may adjust the temporary dispersed storage error encoding parameters 390 based on variations of the loading of the queue with regards to storing another data object 386.

With regards to the non-temporarily storing the data 384 in the DSN 362, the storage module 380, for the data object 386 of the data 384 temporarily stored in the DSN 362 in accordance with the temporary dispersed storage error encoding parameters 390, performs a series of storage steps. In a first storage step, the storage module 380, within the time period corresponding to reliability of the temporary dispersed storage error encoding parameters 390, determines non-temporary storage parameters for the data object. The storage module 380 may determine the time period corresponding to the reliability of the temporary dispersed storage error encoding parameters 390 based on a calculated risk of loss of data due to storage device failures involved when the data 384 is stored using the temporary dispersed storage error encoding parameters 390.

The non-temporary storage parameters include at least one of a level of compression of the data object 386 prior to the error encoding of the data object 386, non-temporary dispersed storage error encoding parameters 390 with an objective of one or more of: long term storage reliability, ease of access, and storage size within the DSN 362, indicating that the first encoded data slices 388 are to be stored as non-temporary data slices (e.g., second encoded data slices 392), and disposition of the first encoded data slices 388 (e.g., keep for another given period of time, delete). The storage module 380 may determine the non-temporary storage parameters by a series of ascertaining steps. A first ascertaining step includes the storage module 380 ascertaining source information regarding a source of the data object 386. A second ascertaining step includes the storage module 380 ascertaining user information regarding a user of the data object 386. A third ascertaining step includes the storage module 380 ascertaining content information regarding content of the data object 386. A fourth ascertaining step includes the storage module 380 determining the non-temporary storage parameters based on at least one of the source information, the user information, and the content information. Alternatively, the storage module 380 establishes the non-temporary storage parameters to correspond to the temporary dispersed storage error encoding parameters 390 when reliability-duration of the temporary dispersed storage error encoding parameters 390 corresponds to a desired reliability-duration of non-temporary storage of the data object 386.

In a second storage step, the storage module 380 retrieves the first encoded data slices 388 regarding the data object 386 from the DSN 362, where the data object 386 was error encoded in accordance with the temporary dispersed storage error encoding parameters 390 to produce the first encoded data slices 388. In a third storage step, the storage module 380 reconstructs the data object 386 from the first encoded data slices 388 in accordance with the temporary dispersed storage error encoding parameters 390. In a fourth storage step, the storage module 380 encodes the reconstructed data object 386 in accordance with the non-temporary storage parameters for the data object 386 to produce the second encoded data slices 392. In a fifth storage step, the storage module 380 stores the second encoded data slices 392 in the DSN 362. For example, the storage module 380 stores the second encoded data slices 392 in the non-temporary memory 366 of the DSN 362. The storage module 380 may delete the first encoded data slices 388 from the temporary memory 364 when the second encoded data slices 392 are stored in the non-temporary memory 366. The storage module 380 may queue processing (e.g., performing the series of storage steps) of the data objects 386 temporarily stored in the DSN 362 in accordance with the temporary dispersed storage error encoding parameters 390 and adjust processing priority of one or more of the data objects 386 based on elapsed time since temporary storage and the time period.

FIG. 40C is a flowchart illustrating an example of non-temporarily storing temporarily stored data. The method begins at step 400 where a processing module (e.g., a dispersed storage processing module of a computing device) receives data from a plurality of sources. The method continues at step 402 where the processing module queues the data for storage in a dispersed storage network (DSN). For example, the processing module extracts one or more data objects from the data and stores each of the one or more data objects in a queue of a memory associated with the computing device. The method continues at step 404 where the processing module determines a loading of a queue corresponding to the queuing of the data to initiate a process to temporarily store the data in the DSN that includes the processing module utilizing temporary dispersed storage error encoding parameters for efficient and reliable error encoded temporary storage of the data in the DSN. The determining may be based on one or more of monitoring a queue size, initiating a query, initiating a test, receiving a response, and receiving an error message.

The method continues at step 406 where the processing module determines a desired reliability-duration of the temporary storage based on the loading (e.g., minimal risk of losing data within a given period of time). The method continues at step 408 where the processing module determines the temporary dispersed storage error encoding parameters based on the loading and the desired reliability-duration. Alternatively, or in addition to, the processing module adjusts the temporary dispersed storage error encoding parameters based on variations of the loading of the queue. For example, the processing module adjusts the temporary dispersed storage error encoding parameters to facilitate faster temporary storage when the loading of the queue indicates that a queue loading level is greater than a high loading level threshold.

The method continues at step 410 where the processing module retrieves a data object of the one or more data objects from the queue. The method continues at step 412 where the processing module encodes the retrieved data object of the data in accordance with the temporary dispersed storage error encoding parameters to produce first encoded data slices. The method continues at step 414 where the processing module stores the first encoded data slices in temporary memory of the DSN. The method continues at step 416 where the processing module adjusts priority of processing of temporarily stored data objects. The adjusting includes queuing processing of the data objects temporarily stored in the DSN in accordance with the temporary dispersed storage error encoding parameters and adjusting the processing priority of one or more of the data objects based on elapsed time since temporary storage and a time period corresponding to reliability of the temporary dispersed storage error encoding parameters. The processing module may determine the time period corresponding to the reliability of the temporary dispersed storage error encoding parameters based on a calculated risk of loss of data due to storage device failures involved when the data is stored using the temporary dispersed storage error encoding parameters.

For the data object of the data temporarily stored in the DSN in accordance with the temporary dispersed storage error encoding parameters, within the time period corresponding to reliability of the temporary dispersed storage error encoding parameters, the method continues at step 418 where the processing module determines non-temporary storage parameters for the data object. The non-temporary storage parameters include at least one of a level of compression of the data object prior to the error encoding of the data object, non-temporary dispersed storage error encoding parameters with an objective of one or more of: long term storage reliability, ease of access, and storage size within the DSN, indicating that the first encoded data slices are to be stored as non-temporary data slices (e.g., the second encoded data slices), and disposition of the first encoded data slices (e.g., keep for a given period of time, delete). The determining non-temporary storage parameters for the data object includes a series of ascertaining steps. A first ascertaining step includes the processing module ascertaining source information regarding a source of the data object (e.g., an identifier associated with an entity that provided the data object). A second ascertaining step includes the processing module ascertaining user information (e.g., user identifier) a regarding a user of the data object. A third ascertaining step includes the processing module ascertaining content information (e.g., content type, content size, content storage priority, a content retrieval reliability requirement) regarding content of the data object. A fourth ascertaining step includes the processing module determining the non-temporary storage parameters based on at least one of the source information, the user information, and the content information. Alternatively, the processing module establishes the non-temporary storage parameters to correspond to the temporary dispersed storage error encoding parameters when reliability-duration of the temporary dispersed storage error encoding parameters corresponds to a desired reliability-duration of non-temporary storage of the data object.

The method continues at step 420 where the processing module retrieves the first encoded data slices regarding the data object from the DSN, where the data object was error encoded in accordance with the temporary dispersed storage error encoding parameters to produce the first encoded data slices. The method continues at step 422 where the processing module reconstructs the data object from the first encoded data slices in accordance with the temporary dispersed storage error encoding parameters. For example, the processing module decodes the first encoded data slices using a dispersed storage error coding function in accordance with the temporary dispersed storage error encoding parameters to produce a reconstructed data object. The method continues at step 424 where the processing module encodes the reconstructed data object in accordance with the non-temporary storage parameters for the data object to produce second encoded data slices. The method continues at step 426 where the processing module stores the second encoded data slices in the DSN. The storing includes the processing module sending the second encoded data slices to a non-temporary memory of the DSN. The method continues at step 428 where the processing module deletes the first encoded data slices from the temporary memory (e.g., when the second encoded data slices are stored in the non-temporary memory).

FIG. 41A is a schematic block diagram of another embodiment of a distributed computing system that includes a user device 14, a load balance module 430, a plurality of distributed storage and task (DST) processing units 16 (DS processing units), and a distributed storage and task network (DSTN) module 22 (DSN module). The DSTN module 22 includes a plurality of DST execution units 36 (storage units). The load balance module 430 may be implemented as at least one of a computer, a computing device, a server, a user device 14, or a DST processing unit 16. The load balance module 430 receives data 432 for storage in the DSTN module 22 from the user device 14. The load balance module 430 determines which one of the plurality of DST processing units 16 to facilitate storage of the data 432 as encoded data slices (slices) 434 in the DSTN module 22 in accordance with a load balanced approach. The load balanced approach may be accomplished in a variety of ways. In a first way, a DSTN address associated with the data 432 is utilized to identify one or more associated DST processing units 16 as a first selection step and the one of the one or more DST processing units 16 is further identified as a final selection step. The identifying the one or more associated DST processing units 16 may be based on accessing an affinity table that associates the DSTN address with the one or more DST processing units 16. The further identifying the one DST processing unit 16 may be based on one or more of DST processing unit availability, DST processing unit priority, or DST processing unit attributes. In a second way, a DST processing unit 16 is selected randomly. In a third way, a DST processing unit 16 is selected based on a requesting entity ID and an association between requesting entity ID and the plurality of DST processing units 16.

In an example of operation, the load balance module 430 receives a data access request that includes the data 432 from user device 14 and determines a DSTN address associated with the data 432 of the data access request. The determining may be based on one or more of a lookup, receiving, or generating a source name based on a data identifier (ID) of the data 432 or a requesting entity ID associated with the user device 14. The load balance module 430 accesses the affinity table utilizing the DSTN address to identify the one or more DST processing units 16 associated with a range of DSTN addresses that includes the DSTN address. The load balance module 430 selects one of the one or more DST processing units 16 based on a priority level associated with the one or more DST processing units 16. For example, the load balance module selects the one DST processing unit 16 when the one DST processing unit 16 is associated with a priority level of 1 when other DST processing units 16 of the one or more DST processing units 16 are associated with priority level numbers of lower priority. The load balance module 430 forwards the data access request to the one selected DST processing unit 16. The one selected DST processing unit 16 receives the data access request and processes the access request with regards to slices 434 stored in the DSTN module 22.

FIG. 41B is a diagram illustrating an example of an affinity table 436 that includes one or more table entries corresponding to one or more distributed storage and task (DST) processing units. Each table entry of the one more table entries includes a distributed storage and task network (DSTN) address range entry of a DSTN address range field 438, a priority entry of a priority field 440, a DST processing unit identifier (ID) entry of a DST processing unit ID field 442, or DST processing unit attributes of a DST processing unit attributes field 444. The DSTN address range entry includes a range of DSTN addresses associated with a DST processing unit of the table entry. The priority entry indicates a priority level associated with the DST processing unit of the entry relative to other DST processing units associated with substantially the same DSTN address range. The DST processing unit ID entry includes an identifier associated with the DST processing unit of the table entry. The DST processing unit attributes include one or more attributes associated with the DST processing unit of the table entry. The attributes include one or more of a physical location identifier, a historic reliability performance level, a historic availability performance level, a loading capacity level, a bandwidth capability level, a processing level indicator, or an historic error rate level. A method of operation of a system to utilize the affinity table is discussed in greater detail with reference to FIG. 41C.

FIG. 41C is a flowchart illustrating an example of load-balancing. The method begins at step 446 where a processing module (e.g., of a load balance module) receives a distributed storage and task network (DSTN) access request. The request may include one or more of a data identifier (ID), data, a DSTN address associated with the data, a request type, or a requesting entity ID. The method continues at step 448 where the processing module identifies a DSTN address of the DSTN access request. The identifying may be based on one or more of extracting the DSTN address from the access request, a lookup (e.g., for a read request type), or generating the DSTN address (e.g., for a write request type) based on one or more of the requesting entity ID, the data, a vault ID associated with the requesting entity, a vault lookup, or the data ID.

The method continues at step 450 where the processing module identifies one or more DST processing units affiliated with the DSTN address. The identifying may be based on one or more of receiving, an affinity table lookup, or a query. For example, the processing module accesses an affinity table based on the DSTN address to identify the one or more DST processing units associated with a DSTN address range that includes the DSTN address. For each of the one or more DST processing units, the method continues at step 452 where the processing module determines an availability level. The determining may be based on one or more of receiving an error message, accessing an availability list, sending a query, or performing a test.

For each of the one or more DST processing units, the method continues at step 454 where the processing module determines a priority level. The determining may be based on one or more of an affinity table lookup, receiving a list, or sending a query. The method continues at step 456 where the processing module selects one DST processing unit of the one or more DST processing units based on one or more of the availability level and the priority level. For example, the processing module selects an available DST processing unit associated with a greater priority level then other available DST processing units. The method continues at step 458 where the processing module forwards the DSTN access request to the selected DST processing unit. Alternatively, or in addition to, the processing module appends the DSTN address to the access request when the request type is a write request and the processing module has generated the DSTN address as a new DSTN address associated with the data.

FIG. 42 is a flowchart illustrating another example of load-balancing, which include similar steps to FIG. 41C. The method begins with steps 446-450 of FIG. 41C where a processing module (e.g., of a load balance module of a computing device) receives a distributed storage and task network (DSTN) access request, identifies a DSTN address of the DSTN access request, and identifies one or more DST processing units affiliated with the DSTN address. The method continues at step 460 where the processing module selects one of the one or more DST processing units based on DST processing unit attributes. The selecting includes obtaining DST processing unit attributes associated with the one or more DST processing units affiliated with the DSTN address. The obtaining may be based on one or more of receiving, a query, or an affinity table lookup. The selecting includes optimizing a match of DST processing unit attributes to the access request. For example, the processing module selects a DST processing unit associated with a superior processing level indicator and an available storage capacity indicator that compares favorably to the access request and to similar metrics of other DST processing units associated with the DSTN address.

The method continues at step 462 where the processing module determines whether the selected DST processing unit is associated with a favorable availability level. The determining may be based on one or more of sending a query, receiving an availability indicator, a DST processing unit status table lookup, or comparing an availability level to an availability level threshold. The processing module determines that the selected DST processing unit is associated with a favorable availability level when the availability level is greater than the availability level threshold. The method branches to step 458 of FIG. 41C when the processing of determines that the selected DST processing unit is associated with the favorable availability level. The method continues to step 464 when the processing of determines that the selected DST processing unit is not associated with the favorable availability level. The method continues at step 464 where the processing module deterministically selects another of the one or more DST processing units affiliated with the DSTN address. For example, the processing module picks an nth DST processing unit affiliated with the DSTN address (e.g., in a circular fashion when n is greater than a number of DST processing units affiliated with the DSTN address). The method loops back to step 462. The method continues at step 458 of FIG. 41C where the processing module forwards the DSTN access request to the selected DST processing unit when the processing module determines that the selected DST processing unit is associated with the favorable availability level.

FIG. 43A is a schematic diagram illustrating another example of load-balancing within a storage network (e.g., DSN/DSTN), which includes one or more similar steps to FIG. 41C. Load balancing module 430 (of a computing device) receives a storage network access request (e.g., R/W of encoded data slices). The load balancing module (processing module) determines whether the storage network access request is write-centric or read-centric based on a request type of the request. The write-centric access request includes at least one of a write slice request and a delete slice request. The read-centric access request includes at least one of a read slice request, a list request, and a list digest request. When the load balancing module determines that the storage network access request is read-centric, one or more read-centric processing units are identified (shown as 16-1 and 16-2). A read-centric processing unit may be associated with a lower processing capability (e.g., look-up a location of requested data in a storage network access request and retrieve from the location)) than a write-centric processing unit (shown as 16-N−1 thru 16-N) where storage of encoded data slices can require additional processing (e.g., free space analysis, address space assignment, mapping, movement of encoded data slices over time, storage error processing, device profile and performance determinations, or device failures, etc.), one or more these processing steps may not be needed by a simple read. The identifying may be based on one or more of a lookup, a predetermination, a query, and a test. Load balancing module 430 determines whether attributes of one of the one or more read-centric processing units meets the minimum requirement(s) level of the storage network access request. The attributes include one or more of an available processing capacity level, available processing capability, a memory availability level, and an available input/output bandwidth level. The determining may be based on comparing an estimated minimum requirement level of the access request to the attributes of the one processing unit 16-1. For example, the load balancing module 430 determines that the minimum requirement level is not one of the attributes of the one processing unit 16-1 that compare favorably to the estimated minimum requirement level and therefore determines that the one DST processing unit 16-1 does not meet the minimum requirement level. Other profile, performance or historical comparison criteria may be substituted when determining the estimated minimum requirement(s) level.

FIG. 43B is a system diagram illustrating the load balancing module identifying one or more write-centric processing units (shown as 16-N−1 thru 16-N). The identifying may be based on one or more of a lookup, a predetermination, a query, and a test. The load balancing module selects one processing unit (16-N) of the one or more write-centric processing units. The selecting may be based on one or more of identifying the one processing unit (16-N) associated with a most favorable level of one or more of availability, memory capacity, capability, and reliability. The load balancing module forwards the storage network access request to the selected processing unit.

FIG. 43C is a flowchart illustrating another example of load-balancing, which include similar steps to FIG. 41C. The method begins with step 446 of FIG. 41C where a processing module (e.g., of a load balance module of a computing device) receives a storage network (e.g., DSN/DSTN) access request. The method continues at step 466 where the processing module determines whether the storage network access request is write-centric or read-centric based on a request type of the request. The write-centric storage network access request includes at least one of a write slice request and a delete slice request. The read-centric storage network access request includes at least one of a read slice request, a list request, and a list digest request. The method branches to step 472 when the processing module determines that the storage network access request is write-centric. The method continues to step 468 when the processing module determines that the storage network access request is read-centric.

The method continues at step 468 where the processing module identifies one or more read-centric storage network processing units. A read-centric storage network processing unit may be associated with a lower processing capability than a write-centric storage network processing unit. The identifying may be based on one or more of a lookup, a predetermination, a query, and a test. The method continues at step 470 where the processing module determines whether attributes of one of the one or more read-centric storage network processing units meets the minimum requirement level of the access request. The attributes includes one or more of an available processing capacity level, available processing capability, a memory availability level, and an available input/output bandwidth level. The determining may be based on comparing an estimated minimum requirement level of the access request to the attributes of the one storage network processing unit. For example, the processing module determines that the minimum requirement level is not one of the attributes of the one storage network processing unit that compare favorably to the estimated minimum requirement level. The method branches to step 458 of FIG. 41C when the processing module determines that the one storage network processing unit meets the minimum requirement level. The method continues to step 472 when the processing module determines that the one storage network processing unit does not meet the minimum requirement level.

The method continues at step 472 where the processing module identifies one or more write-centric storage network processing units. The identifying may be based on one or more of a lookup, a predetermination, a query, and a test. The method continues at step 474 where the processing module selects one storage network processing unit of the one or more write-centric storage network processing units. The selecting may be based on one or more of identifying the one storage network processing unit associated with a most favorable level of one or more of availability, memory capacity, capability, and reliability. The method continues with step 458 of FIG. 41C where the processing module forwards the storage network access request to the selected storage network processing unit.

FIG. 44A is a schematic block diagram of another embodiment of a distributed storage and task (DST) client module 34 that includes a deterministic source name generator 476 and a function selector 478. The deterministic source name generator 476 receives a write request 480 and generates a source name (or identifier) 484 associated with the write request 480 based on a function 482 from the function selector 478. The source name 484 is associated with at least one distributed storage and task network (DSTN) address range of a plurality of DSTN address ranges, where each DSTN address range utilizes a specific set of storage resources within a DSTN module. Each set of storage resources may be associated with a storage attribute. The storage attribute includes one or more of high reliability, low reliability, low cost, high cost, local storage, remote storage, etc. For example, storage resources associated with a first DSTN address range may be associated with above average storage reliability and storage resources associated with a second DSTN address range may be associated with low-cost and average storage reliability.

The function selector 478 receives the write request 480 and selects the function 482 based on one or more of a data identifier (ID) of the write request, a requesting entity ID, an available storage level, a security goal, a lookup, and a reliability goal. For example, the function selector 478 selects a function associated with high storage reliability when the requesting entity ID is associated with a high storage reliability profile. The function selector 478 outputs the function 482 to the deterministic source name generator 476 for utilization in generating the source name 484 based on the write request 480. For example, the deterministic source name generator 476 performs a hashing function on the data ID of the write request 480 to produce an interim result, truncates the interim result to produce a truncated interim result, and adds the truncated interim result to a starting DSTN address of a DSTN address range associated with a desired set of storage resources within the DSTN module to produce the source name 484.

FIG. 44B is a schematic block diagram of another embodiment of a dispersed storage network system that includes a computing device 490, a dispersed storage network (DSN) 492, and a plurality of authorized users 516. An authorized user 516 of the plurality of authorized users 516 includes a user device. Each of the plurality of authorized users 516 provides data objects 508 for storage in the DSN 492. The DSN 492 includes a multitude of storage nodes 496 for storage of the data objects 508 to provide an on-line media storage system. The multitude of storage nodes 496 may be organized into one or more storage node sets 494. Each storage node 496 of the plurality of storage nodes 496 may be implemented utilizing at least one of a storage server, a storage unit, a storage module, a memory device, a memory, a distributed storage (DS) unit, a DS processing unit, a distributed storage and task (DST) execution unit, a user device, a DST processing unit, and a DST processing module.

The computing device 490 includes a dispersed storage (DS) module 498 and a memory 500. The memory 500 may be implemented using one or more of a solid state memory device, an optical disk memory device, and a magnetic disk memory device. The memory 500 may be organized to include a buffer 502 for storage of the data objects 508. For example, an array/first plurality of solid state memory devices of a first memory type provides the buffer 502 to temporarily store the data objects 508. The computing device 490 may be implemented utilizing at least one of a server, a storage unit, a distributed storage and task network (DSTN) managing unit, a DSN managing unit, a DS unit, the storage node 496, a storage server, a storage module, a DS processing unit, a DST execution unit, a user device, a DST processing unit, and a DST processing module. For example, computing device 490 is implemented as the DS processing unit. The DS module 498 includes a receive module 503, a select storage module 504, and a store module 506.

The system functions to receive the data objects 508 from the plurality of authorized users 516, select a set of storage nodes 494 for storage of a data object 508 of the data objects 508, and store the data object 508 in the selected set of storage nodes 494. With regards to the receiving the data objects 508 from the plurality of authorized users 516, the receive module 503 randomly and continuously receives, for storage in the on-line media storage system (e.g., the DSN 492), the data objects 508 from the plurality of authorized users 516, where a data type (e.g., text, music, voice, image, video) of the data object 508 of the data objects 508 is one of a plurality of different data types and where memory space within the on-line media storage system is primarily allocated to the plurality of authorized users 516 on an as-needed basis. As such, an authorized user 516 of the plurality of authorized users 516 has no minimal pre-allocated memory space within the on-line media storage system. The receive module 503, prior to processing the data objects 508, temporarily stores the data objects 508 in the buffer 502 and retrieves the data objects 508 from the buffer 502 in accordance with a priority protocol to process the data objects 508. The priority protocol includes at least one of a first in first out approach, a parallel processing approach, a last in first out approach, and a random approach. The receive module 503 may output the data objects 508 retrieved from the buffer 502 to the select storage module 504 for the processing of the data objects 508. Alternatively, the receive module 503 immediately forwards the data objects 508 received from the plurality of authorized users 516 directly to the select storage module 504.

With regards to the selecting the set of storage nodes 494 for storage of the data object 508, the select storage module 504 processes the data objects 508 for storage by entering a loop that includes a series of loop steps. In a first loop step, for the data object 508 from the authorized user 516 of the plurality of authorized users 516, the select storage module 504 determines a system level storage efficiency preference based on system storage node information and one or more of: the data type of the data object 508, data size of the data object 508, identity of the authorized user 516, location of the authorized user 516, system privileges of the authorized user 516, storage preferences of the authorized user 516, and user group affiliation of the authorized user 516. The system storage node information includes one or more of memory utilization of each of the multitude of storage nodes 496, available memory of each of the multitude of storage nodes 496, allocated data type storage preference of each of the multitude of storage nodes 496 (e.g., a storage node is designated as primarily storing video, but can store other data types), access history of each of the multitude of storage nodes 496 (e.g., access speeds, on-line percentage, failure rates), geographic location of each of the multitude of storage nodes 496, and physical characteristics of each of the multitude of storage nodes 496 (e.g., hardware configuration, software configuration, network connections, Internet protocol address, memory capacity level, memory utilization level).

The select storage module 504 determines the system level storage efficiency preferences by a series of preference steps. A first preference step includes the select storage module 504 interpreting one or more data attributes associated with a data object, such as: the data type of the data object 508, the data size of the data object 508, the identity of the authorized user 516, the location of the authorized user 516, the system privileges of the authorized user 516, the storage preferences of the authorized user 516, and the user group affiliation of the authorized user 516 to produce a user storage preference. A second preference step includes the select storage module 504 identifying a preliminary set of storage nodes 494 (e.g., of many candidate storage node sets 494) based on the user storage preference. A third preference step includes the select storage module 504 determining preliminary dispersed storage error encoding parameters based on the user storage preference. A fourth preference step includes the select storage module 504 determining the system storage node information for the preliminary set of storage nodes. When the system storage node information indicates that the preliminary set of storage nodes is appropriate for storing encoded data slices, a fifth preference step includes the select storage module 504 utilizing the preliminary set of storage nodes for the set of storage nodes 494 and utilizing the preliminary dispersed storage error encoding parameters as dispersed storage error encoding parameters 514. When the system storage node information indicates that the preliminary set of storage nodes is not appropriate for storing the encoded data slices, a sixth preference step includes the select storage module 504 selecting the set of storage nodes 494 based on a compromise between the user storage preference and the system storage node information of the set of storage nodes.

In a second loop step, the select storage module 504 selects the set of storage nodes 494 from a multitude of storage nodes 496 of the on-line media storage system based on the system level storage efficiency preference (e.g., best match) to provide a storage node selection 512. In a third loop step, the select storage module 504 determines the dispersed storage error encoding parameters based on the set of storage nodes (e.g., a mapping lookup) or based on the system level storage efficiency preference. In an example of operation of the select storage module 504, the select storage module 504 determines the data type of the data object 508 to be a video file and determines geographic location of the authorized user. Next, the select storage module 504 prioritizes allocated data type storage preference and geographic location of the system level storage efficiency preference and selects the set of storage nodes primarily based on the data object being the video file and the geographic location of the authorized user. Next, the select storage module 504 determines the dispersed storage error encoding parameters 514 primarily based on the data object being the video file.

With regards to the storing of the data object 508 in the selected set of storage nodes 494, the store module 506 processes the data objects 508 for storage by continuing the loop that includes further loop steps of the series of loop steps. In a fourth loop step, the store module 506 encodes the data object 508 in accordance with the dispersed storage error encoding parameters 514 to produce the encoded data slices. In a fifth loop step, the store module 506 generates system addressing information for the encoded data slices based on the encoded data slices, the set of storage nodes 494, and identity of the data object 508.

The store module 506 generates the system addressing information for the encoded data slices by generating, as the system addressing information, slice names for the encoded data slices, where a slice name of the slice names includes identity of one of the encoded data slices, an address of a range of addresses assigned to a storage node 494 of the set of storage nodes 494, and identity of the data object 508. For example, the store module 506 selects a deterministic function based on one or more of the data object 508, the data type of the data object 508, the data size of the data object 508, the identity of the authorized user 516, the location of the authorized user 516, the system privileges of the authorized user 516, the storage preferences of the authorized user 516, the user group affiliation of the authorized user 516, the data object size indicator, a data object type indicator, the identifier (ID) of the data object, a vault identifier associated with the authorized user 516, a registry lookup, a vault lookup, a DSN status indicator, and a storage requirement. The deterministic function includes one or more of a finite field arithmetic function, a hashing function, a cyclic redundancy code function, a hash based message authentication code function, a mask generating function, and a sponge function. Next, the store module 506 transforms the ID of the data object using the deterministic function to produce a transformed data object ID. The transforming includes applying the deterministic function to the ID of the data object to produce the transformed data object ID. Next, the store module 506 generates the system addressing information (e.g., address) using the transformed data object ID, where the address falls within the range of addresses assigned to the storage node of the set of storage nodes. For instance, the generating includes using the transformed data object ID as an offset from a starting address of the range of addresses.

In a sixth loop step, the store module 506 updates a user profile (e.g., a directory entry, a dispersed hierarchical index entry) for the authorized user 516 to include the system addressing information for the data object 508. In a seventh loop step, the store module 506 issues write commands 510 to the set of storage nodes 494 for storing the encoded data slices in the set of storage nodes 494 (e.g., in a second plurality of memory devices of a second memory type that differs from the first plurality of memory devices of the buffer 502). The issuing includes generating the write commands 510 to include the encoded data slices and the slice names for the encoded data slices. The issuing further includes sending the write commands 510 to the set of storage nodes 494. Next, the loop is repeated by the select storage module 504 and the store module 506 for another data object 508 from another authorized user 516 of the plurality of authorized users 516 as the data object 508 from the authorized user 516.

FIG. 44C is a flowchart illustrating an example of storing. The method begins at step 520 where a processing module (e.g., of a dispersed storage (DS) processing module of one or more computing devices associated with an on-line media storage system) randomly and continuously receives, for storage in the on-line media storage system, data objects from a plurality of authorized users. A data type of a data object of the data objects is one of a plurality of different data types and memory space within the on-line media storage system that is primarily allocated to the plurality of authorized users on an as-needed basis. The method continues at step 522 where the processing module, prior to processing the data objects, temporary stores the data objects in a buffer (e.g., a first plurality of memory devices, of a first memory type, associated with the processing module). The method continues at step 524 where the processing module retrieves the data objects from the buffer in accordance with a priority protocol to process the data objects. For a data object from an authorized user of the plurality of authorized users, the method continues at step 526 where the processing module determines a system level storage efficiency preference based on system storage node information and one or more of: the data type of the data object, data size of the data object, identity of the authorized user, location of the authorized user, system privileges of the authorized user, storage preferences of the authorized user, and user group affiliation of the authorized user.

The determining the system level storage efficiency preference includes a series of preference determining steps. A first preference determining step includes interpreting one or more of: the data type of the data object, the data size of the data object, the identity of the authorized user, the location of the authorized user, the system privileges of the authorized user, the storage preferences of the authorized user, and the user group affiliation of the authorized user to produce a user storage preference. A second preference determining step includes identifying a preliminary set of storage nodes based on the user storage preference. A third preference determining step includes determining preliminary dispersed storage error encoding parameters based on the user storage preference. A fourth preference determining step includes determining the system storage node information for the preliminary set of storage nodes. When the system storage node information indicates that the preliminary set of storage nodes is appropriate for storing the encoded data slices, a fifth preference determining step includes utilizing the preliminary set of storage nodes for the set of storage nodes and utilizing the preliminary dispersed storage error encoding parameters as the dispersed storage error encoding parameters. When the system storage node information indicates that the preliminary set of storage nodes is not appropriate for storing the encoded data slices, a six preference determining step includes selecting the set of storage nodes based on a compromise between the user storage preference and the system storage node information of the set of storage nodes.

The method continues at step 528 where the processing module selects a set of storage nodes from a multitude of storage nodes of the on-line media storage system based on the system level storage efficiency preference. Alternatively, the processing module identifies two or more candidate sets of storage nodes from a multitude of storage nodes based on the system level storage efficiency preference, where each of the two or more sets of candidate storage nodes compare favorably to the system level storage efficiency preference. The method continues at step 530 where the processing module determines dispersed storage error encoding parameters based on the set of storage nodes or based on the system level storage efficiency preference. For example, the processing module determines the dispersed storage error encoding parameters to include a pillar width of 16 when the set of storage nodes includes 16 storage nodes. As another example, the processing module determines the dispersed storage error encoding parameters to include a complementary pairing of the pillar width and a decode threshold to provide a level of retrieval reliability that compares favorably to the system level storage efficiency preference. The method continues at step 532 where the processing module encodes the data object in accordance with the dispersed storage error encoding parameters to produce encoded data slices. For example, the processing module encodes the data object using a dispersed storage error coding function in accordance with the dispersed storage error encoding parameters to produce the encoded data slices that includes a plurality of sets of encoded data slices.

The method continues at step 534 where the processing module generates system addressing information for the encoded data slices based on the encoded data slices, the set of storage nodes, and identity of the data object. The generating the system addressing information for the encoded data slices further includes generating, as the system addressing information, slice names for the encoded data slices, wherein a slice name of the slice names includes identity of one of the encoded data slices, an address of a range of addresses assigned to a storage node of the set of storage nodes, and identity of the data object. Alternatively, the processing module selects one set of storage nodes of the two or more sets of candidate storage nodes when the two or more sets of candidate storage nodes have been selected, where the selecting of the one set of storage nodes is based on the generating of the addressing information. For example, the processing module generates the slice names for the encoded data slices utilizing a deterministic function to select the address of the range of addresses assigned to storage nodes of the two or more candidate sets of storage nodes. For instance, the processing module performs a deterministic function on the identity of the data object to produce an offset and adds the offset to a starting address of the range of addresses to produce the address.

The method continues at step 536 where the processing module updates a user profile for the authorized user to include the system addressing information for the data object. For example, the processing module updates an entry of at least one of a directory and a dispersed hierarchical index to associate the identity of the data object and the system addressing information, where the entry is associated with the authorized user. The method loops back to step 526 for another data object from another authorized user of the plurality of authorized users as the data object from the authorized user.

In an example of operation, the processing module determines the data type of the data object to be a video file. Next, the processing module determines geographic location of the authorized user and prioritizes allocated data type storage preference and geographic location of the system level storage efficiency preference. Next, the processing module selects the set of storage nodes primarily based on the data object being the video file and the geographic location of the authorized user and determines the dispersed storage error encoding parameters primarily based on the data object being the video file.

It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, text, graphics, audio, etc. any of which may generally be referred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. For some industries, an industry-accepted tolerance is less than one percent and, for other industries, the industry-accepted tolerance is 10 percent or more. Other examples of industry-accepted tolerance range from less than one percent to fifty percent. Industry-accepted tolerances correspond to, but are not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, thermal noise, dimensions, signaling errors, dropped packets, temperatures, pressures, material compositions, and/or performance metrics. Within an industry, tolerance variances of accepted tolerances may be more or less than a percentage level (e.g., dimension tolerance of less than +/−1%). Some relativity between items may range from a difference of less than a percentage level to a few percent. Other relativity between items may range from a difference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., indicates an advantageous relationship that would be evident to one skilled in the art in light of the present disclosure, and based, for example, on the nature of the signals/items that are being compared. As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide such an advantageous relationship and/or that provides a disadvantageous relationship. Such an item/signal can correspond to one or more numeric values, one or more measurements, one or more counts and/or proportions, one or more types of data, and/or other information with attributes that can be compared to a threshold, to each other and/or to attributes of other information to determine whether a favorable or unfavorable comparison exists. Examples of such a advantageous relationship can include: one item/signal being greater than (or greater than or equal to) a threshold value, one item/signal being less than (or less than or equal to) a threshold value, one item/signal being greater than (or greater than or equal to) another item/signal, one item/signal being less than (or less than or equal to) another item/signal, one item/signal matching another item/signal, one item/signal substantially matching another item/signal within a predefined or industry accepted tolerance such as 1%, 5%, 10% or some other margin, etc. Furthermore, one skilled in the art will recognize that such a comparison between two items/signals can be performed in different ways. For example, when the advantageous relationship is that signal 1 has a greater magnitude than signal 2, a favorable comparison may be achieved when the magnitude of signal 1 is greater than that of signal 2 or when the magnitude of signal 2 is less than that of signal 1. Similarly, one skilled in the art will recognize that the comparison of the inverse or opposite of items/signals and/or other forms of mathematical or logical equivalence can likewise be used in an equivalent fashion. For example, the comparison to determine if a signal X>5 is equivalent to determining if −X<−5, and the comparison to determine if signal A matches signal B can likewise be performed by determining −A matches −B or not(A) matches not(B). As may be discussed herein, the determination that a particular relationship is present (either favorable or unfavorable) can be utilized to automatically trigger a particular action. Unless expressly stated to the contrary, the absence of that particular condition may be assumed to imply that the particular action will not automatically be triggered. In other examples, the determination that a particular relationship is present (either favorable or unfavorable) can be utilized as a basis or consideration to determine whether to perform one or more actions. Note that such a basis or consideration can be considered alone or in combination with one or more other bases or considerations to determine whether to perform the one or more actions. In one example where multiple bases or considerations are used to determine whether to perform one or more actions, the respective bases or considerations are given equal weight in such determination. In another example where multiple bases or considerations are used to determine whether to perform one or more actions, the respective bases or considerations are given unequal weight in such determination.

As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, “processing circuitry”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, processing circuitry, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, processing circuitry, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, processing circuitry, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, processing circuitry and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, processing circuitry and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with one or more other routines. In addition, a flow diagram may include an “end” and/or “continue” indication. The “end” and/or “continue” indications reflect that the steps presented can end as described and shown or optionally be incorporated in or otherwise used in conjunction with one or more other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained.

The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, a quantum register or other quantum memory and/or any other device that stores data in a non-transitory manner. Furthermore, the memory device may be in a form of a solid-state memory, a hard drive memory or other disk storage, cloud memory, thumb drive, server memory, computing device memory, and/or other non-transitory medium for storing data. The storage of data includes temporary storage (i.e., data is lost when power is removed from the memory element) and/or persistent storage (i.e., data is retained when power is removed from the memory element). As used herein, a transitory medium shall mean one or more of: (a) a wired or wireless medium for the transportation of data as a signal from one computing device to another computing device for temporary storage or persistent storage; (b) a wired or wireless medium for the transportation of data as a signal within a computing device from one element of the computing device to another element of the computing device for temporary storage or persistent storage; (c) a wired or wireless medium for the transportation of data as a signal from one computing device to another computing device for processing the data by the other computing device; and (d) a wired or wireless medium for the transportation of data as a signal within a computing device from one element of the computing device to another element of the computing device for processing the data by the other element of the computing device. As may be used herein, a non-transitory computer readable memory is substantially equivalent to a computer readable memory. A non-transitory computer readable memory can also be referred to as a non-transitory computer readable storage medium.

While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations. 

What is claimed is:
 1. A method for execution by one or more computing devices of a storage network, the method comprises: receiving a data object for storage in memory of the storage network; selecting a set of storage nodes of a plurality of sets of storage nodes for storage of the data object, including: determining storage attributes associated with each set of storage nodes of the plurality of sets of storage nodes; determining a storage preference associated with the data object; and comparing the storage preference with the storage attributes; and facilitating storage of the data object in the selected set of storage nodes.
 2. The method of claim 1, wherein facilitating storage of the data object in the selected set of storage nodes includes: determining dispersed storage error encoding parameters based, at least in part, on the selected set of storage nodes; encoding the data object in accordance with the dispersed storage error encoding parameters to generate a plurality of encoded data slices; and forwarding the plurality of encoded data slices to the selected set of storage nodes for storage therein.
 3. The method of claim 2, wherein determining dispersed storage error encoding parameters is further based on a retrieval reliability level associated with the data object.
 4. The method of claim 2, further comprising: temporarily storing the received data object in a buffer of the storage network, wherein facilitating storage of the data object in the selected set of storage nodes further includes retrieving the data object from the buffer.
 5. The method of claim 1, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to a storage reliability level.
 6. The method of claim 1, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to an input/output bandwidth level.
 7. The method of claim 1, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to a memory type of a plurality of memory types.
 8. The method of claim 1, wherein the storage preference associated with the data object includes at least one of: a data type of the data object; a data size of the data object; a system privilege of an authorized user of the storage network; a storage preference of an authorized user of the storage network; or a user group affiliation of an authorized user of the storage network.
 9. A computing device comprises: an interface; memory that stores operational instructions; and a processing module operably coupled to the interface and the memory, wherein the processing module is configured to execute the operational instructions to: receive, via the interface, a data object for storage in memory of a storage network; select a set of storage nodes of a plurality of sets of storage nodes for storage of the data object, including: determining storage attributes associated with each set of storage nodes of the plurality of sets of storage nodes; determining a storage preference associated with the data object; and comparing the storage preference with the storage attributes; and facilitate storage of the data object in the selected set of storage nodes.
 10. The computing device of claim 9, wherein facilitating storage of the data object in the selected set of storage nodes includes: determining dispersed storage error encoding parameters based, at least in part, on the selected set of storage nodes; encoding the data object in accordance with the dispersed storage error encoding parameters to generate a plurality of encoded data slices; and forwarding the plurality of encoded data slices to the selected set of storage nodes for storage therein.
 11. The computing device of claim 10, wherein determining dispersed storage error encoding parameters is further based on a retrieval reliability level associated with the data object.
 12. The computing device of claim 10, wherein the processing module is further configured to execute the operational instructions to: temporarily store the received data object in a buffer of the storage network, wherein facilitating storage of the data object in the selected set of storage nodes further includes retrieving the data object from the buffer.
 13. The computing device of claim 9, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to a storage reliability level.
 14. The computing device of claim 9, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to an input/output bandwidth level.
 15. The computing device of claim 9, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to a memory type of a plurality of memory types.
 16. The computing device of claim 9, wherein the storage preference associated with the data object includes at least one of: a data type of the data object; a data size of the data object; a system privilege of an authorized user of the storage network; a storage preference of an authorized user of the storage network; or a user group affiliation of an authorized user of the storage network.
 17. A computer readable storage medium comprises: at least one memory section that stores operational instructions that, when executed by one or more processing modules of a computing device of a storage network, causes the computing device to: receive a data object for storage in memory of the storage network; select a set of storage nodes of a plurality of sets of storage nodes for storage of the data object, including: determining storage attributes associated with each set of storage nodes of the plurality of sets of storage nodes; determining a storage preference associated with the data object; and comparing the storage preference with the storage attributes; facilitate storage of the data object in the selected set of storage nodes; and generate system addressing information for the data object.
 18. The computing readable storage medium of claim 17, wherein facilitating storage of the data object in the selected set of storage nodes includes: determining dispersed storage error encoding parameters based, at least in part, on the selected set of storage nodes; encoding the data object in accordance with the dispersed storage error encoding parameters to generate a plurality of encoded data slices; and forwarding the plurality of encoded data slices to the selected set of storage nodes for storage therein.
 19. The computing readable storage medium of claim 17, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to a storage reliability level.
 20. The computing readable storage medium of claim 17, wherein the storage attributes associated with each set of storage nodes correspond, at least in part, to an input/output bandwidth level. 